BibBase

Keyword: speech technology

2023 (1)

Bias in Automatic Speech Recognition: The Case of African American Language. Martin, J. L & Wright, K. E. Applied Linguistics, 44(4):613–630, August, 2023.

Bias in Automatic Speech Recognition: The Case of African American Language [link]

Paper doi abstract bibtex

@article{martin_bias_2023,
	title = {Bias in {Automatic} {Speech} {Recognition}: {The} {Case} of {African} {American} {Language}},
	volume = {44},
	issn = {0142-6001, 1477-450X},
	shorttitle = {Bias in {Automatic} {Speech} {Recognition}},
	url = {https://academic.oup.com/applij/article/44/4/613/6901317},
	doi = {10.1093/applin/amac066},
	abstract = {Abstract
            Research on bias in artificial intelligence has grown exponentially in recent years, especially around racial bias. Many modern technologies which impact people’s lives have been shown to have significant racial biases, including automatic speech recognition (ASR) systems. Emerging studies have found that widely-used ASR systems function much more poorly on the speech of Black people. Yet, this work is limited because it lacks a deeper consideration of the sociolinguistic literature on African American Language (AAL). In this paper, then, we seek to integrate AAL research into these endeavors to analyze ways in which ASRs might be biased against the linguistic features of AAL and how the use of biased ASRs could prove harmful to speakers of AAL. Specifically, we (1) provide an overview of the ways in which AAL has been discriminated against in the workforce and healthcare in the past, and (2) explore how introducing biased ASRs in these areas could perpetuate or even deepen linguistic discrimination. We conclude with a number of questions for reflection and future work, offering this document as a resource for cross-disciplinary collaboration.},
	language = {en},
	number = {4},
	urldate = {2024-02-18},
	journal = {Applied Linguistics},
	author = {Martin, Joshua L and Wright, Kelly Elizabeth},
	month = aug,
	year = {2023},
	keywords = {Automatic Speech Recognition, Speech Technology},
	pages = {613--630},
}

2022 (1)

Bias in Automatic Speech Recognition: The Case of African American Language. Martin, J. L & Wright, K. E. Applied Linguistics, December, 2022.

Paper doi abstract bibtex 1 download

@article{martin_bias_2022,
	title = {Bias in {Automatic} {Speech} {Recognition}: {The} {Case} of {African} {American} {Language}},
	issn = {0142-6001, 1477-450X},
	shorttitle = {Bias in {Automatic} {Speech} {Recognition}},
	url = {https://academic.oup.com/applij/advance-article/doi/10.1093/applin/amac066/6901317},
	doi = {10.1093/applin/amac066},
	abstract = {Abstract
            Research on bias in artificial intelligence has grown exponentially in recent years, especially around racial bias. Many modern technologies which impact people’s lives have been shown to have significant racial biases, including automatic speech recognition (ASR) systems. Emerging studies have found that widely-used ASR systems function much more poorly on the speech of Black people. Yet, this work is limited because it lacks a deeper consideration of the sociolinguistic literature on African American Language (AAL). In this paper, then, we seek to integrate AAL research into these endeavors to analyze ways in which ASRs might be biased against the linguistic features of AAL and how the use of biased ASRs could prove harmful to speakers of AAL. Specifically, we (1) provide an overview of the ways in which AAL has been discriminated against in the workforce and healthcare in the past, and (2) explore how introducing biased ASRs in these areas could perpetuate or even deepen linguistic discrimination. We conclude with a number of questions for reflection and future work, offering this document as a resource for cross-disciplinary collaboration.},
	language = {en},
	urldate = {2023-04-07},
	journal = {Applied Linguistics},
	author = {Martin, Joshua L and Wright, Kelly Elizabeth},
	month = dec,
	year = {2022},
	keywords = {Automatic Speech Recognition, Speech Technology},
	pages = {amac066},
}

2021 (1)

A thorough evaluation of the Language Environment Analysis (LENA) system. Cristia, A., Lavechin, M., Scaff, C., Soderstrom, M., Rowland, C., Räsänen, O., Bunce, J., & Bergelson, E. Behavior Research Methods, 53(2):467–486, April, 2021.

A thorough evaluation of the Language Environment Analysis (LENA) system [link]

Paper doi abstract bibtex

@article{cristia_thorough_2021,
	title = {A thorough evaluation of the {Language} {Environment} {Analysis} ({LENA}) system},
	volume = {53},
	issn = {1554-3528},
	url = {https://doi.org/10.3758/s13428-020-01393-5},
	doi = {10.3758/s13428-020-01393-5},
	abstract = {In the previous decade, dozens of studies involving thousands of children across several research disciplines have made use of a combined daylong audio-recorder and automated algorithmic analysis called the LENAⓇ system, which aims to assess children’s language environment. While the system’s prevalence in the language acquisition domain is steadily growing, there are only scattered validation efforts on only some of its key characteristics. Here, we assess the LENAⓇ system’s accuracy across all of its key measures: speaker classification, Child Vocalization Counts (CVC), Conversational Turn Counts (CTC), and Adult Word Counts (AWC). Our assessment is based on manual annotation of clips that have been randomly or periodically sampled out of daylong recordings, collected from (a) populations similar to the system’s original training data (North American English-learning children aged 3-36 months), (b) children learning another dialect of English (UK), and (c) slightly older children growing up in a different linguistic and socio-cultural setting (Tsimane’ learners in rural Bolivia). We find reasonably high accuracy in some measures (AWC, CVC), with more problematic levels of performance in others (CTC, precision of male adults and other children). Statistical analyses do not support the view that performance is worse for children who are dissimilar from the LENAⓇ original training set. Whether LENAⓇ results are accurate enough for a given research, educational, or clinical application depends largely on the specifics at hand. We therefore conclude with a set of recommendations to help researchers make this determination for their goals.},
	language = {en},
	number = {2},
	urldate = {2023-02-06},
	journal = {Behavior Research Methods},
	author = {Cristia, Alejandrina and Lavechin, Marvin and Scaff, Camila and Soderstrom, Melanie and Rowland, Caroline and Räsänen, Okko and Bunce, John and Bergelson, Elika},
	month = apr,
	year = {2021},
	keywords = {Adult Word Count, Agreement, Child Vocalization Count, Conversational Turn Count, English, Human transcription, LENA, Measurement error, Method comparison, Reliability, Speech technology, Tsimane’},
	pages = {467--486},
}

2015 (1)

Automatic detection and annotation of disfluencies in spoken French corpora. Christodoulides, G. & Avanzi, M. In Interspeech 2015. Proceedings of the 16th Annual Conference of the Internationational Speech Communication Association, pages 1849–1853, 2015.

Automatic detection and annotation of disfluencies in spoken French corpora [link]

Paper abstract bibtex

@inproceedings{christodoulides_automatic_2015,
	Author = {Christodoulides, George and Avanzi, Mathieu},
	Booktitle = {Interspeech 2015. Proceedings of the 16th Annual Conference of the Internationational Speech Communication Association},
	Date = {2015},
	Date-Modified = {2018-07-20 18:49:25 +0000},
	Eventdate = {2015-09-06/2015-09-10},
	Keywords = {labelling and annotation, language resources, disfluencies, French, phonetics, speaking styles, speech technology, spontaneous speech, speech corpora},
	Location = {Dresden, Germany},
	Pages = {1849--1853},
	Title = {Automatic detection and annotation of disfluencies in spoken French corpora},
	Url = {http://www.isca-speech.org/archive/interspeech_2015/i15_1849.html},
	Year = {2015},
	Abstract = {In this paper we propose a multi-step system for the semi-automatic detection and annotation of disfluencies in spoken corpora. A set of rules, statistical models and machine learning techniques are applied to the input, which is a transcription aligned to the speech signal. The system uses the results of an automatic estimation of prosodic, part-of-speech and shallow syntactic features. We present a detailed coding scheme for simple disfluencies (filled pauses, mispronunciations, false starts, drawls and intra-word pauses), structured disfluencies (repetitions, deletions, substitutions, insertions) and complex disfluencies. The system is trained and evaluated on a transcribed corpus of spontaneous French speech, consisting of 112 different speakers and balanced for speaker age and sex, covering 14 different varieties of French spoken in Belgium, France and Switzerland.},
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	Bdsk-Url-1 = {http://www.isca-speech.org/archive/interspeech_2015/i15_1849.html}}

2013 (1)

Paralinguistics in speech and language - State-of-the-art and the challenge. Schuller, B. W., Steidl, S., Batliner, A., Burkhardt, F., Devillers, L., Müller, C. A., & Narayanan, S. S. Computer Speech & Language, 27(1):4–39, 2013.

Paralinguistics in speech and language - State-of-the-art and the challenge [link]

Paper doi bibtex

@article{DBLP:journals/csl/SchullerSBBDMN13,
  author    = {Bj{\"{o}}rn W. Schuller and
               Stefan Steidl and
               Anton Batliner and
               Felix Burkhardt and
               Laurence Devillers and
               Christian A. M{\"{u}}ller and
               Shrikanth S. Narayanan},
  title     = {Paralinguistics in speech and language - State-of-the-art and the
               challenge},
  journal   = {Computer Speech {\&} Language},
  volume    = {27},
  number    = {1},
  pages     = {4--39},
  year      = {2013},
  url       = {https://doi.org/10.1016/j.csl.2012.02.005},
  doi       = {10.1016/j.csl.2012.02.005},
  timestamp = {Tue, 03 Dec 2019 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/bib/journals/csl/SchullerSBBDMN13},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

2012 (2)

Production of filled pauses in concatenative speech synthesis based on the underlying fluent sentence. Adell, J., Escudero, D., & Bonafonte, A. Speech Communication, 54(3):459–476, 2012.
doi abstract bibtex

@article{adell_production_2012,
	Author = {Adell, Jordi and Escudero, David and Bonafonte, Antonio},
	Date = {2012},
	Date-Modified = {2018-05-13 21:55:54 +0000},
	Doi = {10.1016/j.specom.2011.10.010},
	Journal = {Speech Communication},
	Keywords = {disfluencies, f0, filled pauses, pause duration, pauses, phonetics, prosody, repetitions, Spanish, speaking styles, speech synthesis, speech technology, temporal factors, text-to-speech},
	Number = {3},
	Pages = {459--476},
	Title = {Production of filled pauses in concatenative speech synthesis based on the underlying fluent sentence},
	Volume = {54},
	Year = {2012},
	Abstract = {Until now, speech synthesis has mainly involved reading-style speech. Today, however, text-to-speech systems must provide a variety of styles because users expect these interfaces to do more than just read information. If synthetic voices must be integrated into future technology, they must simulate the way people talk instead of the way people read. Existing knowledge about how disfluencies occur has made it possible to propose a general framework for synthesising disfluencies. We propose a model based on the definition of disfluency and the concept of underlying fluent sentences. The model incorporates the parameters of standard prosodic models for fluent speech with local modifications of prosodic parameters near the interruption point. The constituents of the local models for filled pauses are derived from the analysis corpus, and constituent's prosodic parameters are predicted via linear regression analysis. We also discuss the implementation details of the model when used in a real speech synthesis system. Objective and perceptual evaluations showed that the proposed models outperformed the baseline model. Perceptual evaluations of the system showed that it is possible to synthesise filled pauses without decreasing the overall naturalness of the system, and users stated that the speech produced is even more natural than the one produced without filled pauses.},
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	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.specom.2011.10.010}}

Prosodic and temporal features for language modeling for dialog. Ward, N. G., Vega, A., & Baumann, T. Speech Communication, 54(2):161 - 174, 2012.

Prosodic and temporal features for language modeling for dialog [link]

Paper doi bibtex

@article {Ward2012161,
	title = {Prosodic and temporal features for language modeling for dialog},
	journal = {Speech Communication},
	volume = {54},
	number = {2},
	year = {2012},
	pages = {161 - 174},
	keywords = {undefined},
	issn = {0167-6393},
	doi = {10.1016/j.specom.2011.07.009},
	url = {http://www.sciencedirect.com/science/article/pii/S0167639311001166},
	author = {Nigel G. Ward and Alejandro Vega and Baumann, Timo}
}

2011 (1)

Human Voice Recognition Depends on Language Ability. Perrachione, T., K., Del Tufo, S., N., & Gabrieli, J., D., E. Science, 333(6042):595-595, 7, 2011.

Human Voice Recognition Depends on Language Ability [link]

Website abstract bibtex

@article{
 title = {Human Voice Recognition Depends on Language Ability},
 type = {article},
 year = {2011},
 identifiers = {[object Object]},
 pages = {595-595},
 volume = {333},
 websites = {http://www.sciencemag.org/content/333/6042/595.abstract},
 month = {7},
 day = {28},
 id = {c34da379-541e-3931-9160-19f51b1bd965},
 created = {2011-08-01T08:41:11.000Z},
 accessed = {2011-07-29},
 file_attached = {false},
 profile_id = {50edede7-0a24-356e-8f8a-d03397fa3277},
 group_id = {a84e3e74-b76a-3098-bae1-c8a78c436569},
 last_modified = {2017-03-14T14:02:54.442Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Perrachione2011},
 private_publication = {false},
 abstract = {The ability to recognize people by their voice is an important social behavior. Individuals differ in how they pronounce words, and listeners may take advantage of language-specific knowledge of speech phonology to facilitate recognizing voices. Impaired phonological processing is characteristic of dyslexia and thought to be a basis for difficulty in learning to read. We tested voice-recognition abilities of dyslexic and control listeners for voices speaking listeners’ native language or an unfamiliar language. Individuals with dyslexia exhibited impaired voice-recognition abilities compared with controls only for voices speaking their native language. These results demonstrate the importance of linguistic representations for voice recognition. Humans appear to identify voices by making comparisons between talkers’ pronunciations of words and listeners’ stored abstract representations of the sounds in those words.},
 bibtype = {article},
 author = {Perrachione, T. K. and Del Tufo, S. N. and Gabrieli, J. D. E.},
 journal = {Science},
 number = {6042}
}

2010 (4)

Modelling filled pauses prosody to synthesise disfluent speech. Adell, J., Bonafonte, A., & Escudero, D. In Speech Prosody 2010. Proceedings of the 5th International Conference on Speech Prosody, pages 1–4, 2010.

Modelling filled pauses prosody to synthesise disfluent speech [link]

Paper abstract bibtex

@inproceedings{adell_modelling_2010,
	Author = {Adell, Jordi and Bonafonte, Antonio and Escudero, David},
	Booktitle = {Speech Prosody 2010. Proceedings of the 5th International Conference on Speech Prosody},
	Date = {2010},
	Date-Modified = {2018-05-13 21:55:14 +0000},
	Eventdate = {2010-05-11/2010-05-14},
	Keywords = {conversation, disfluencies, duration, filled pauses, pause duration, pauses, phonetics, prosody, rhythm, segmental lengthening, Spanish, speaking styles, speech synthesis, speech technology, temporal factors, text-to-speech},
	Location = {Chicago, IL, USA},
	Pages = {1--4},
	Title = {Modelling filled pauses prosody to synthesise disfluent speech},
	Url = {http://www.isca-speech.org/archive/sp2010/sp10_624.html},
	Year = {2010},
	Abstract = {In the present paper we present a new approach to the synthesis of filled pauses since they are as frequent as most frequent words in conversational speech. The problem is tackled from the point of view of disfluent speech synthesis. Based on the synthetic disfluent speech model, we analyse the features that describe filled pauses and propose a model to predict them. The model was implemented and perceptually evaluated with successful results.},
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	Bdsk-Url-1 = {http://www.isca-speech.org/archive/sp2010/sp10_624.html}}

Multimodal emotion recognition in speech-based interaction using facial expression, body gesture and acoustic analysis. Kessous, L., Castellano, G., & Caridakis, G. Journal on Multimodal User Interfaces, 3(1-2):33--48, March, 2010.

Multimodal emotion recognition in speech-based interaction using facial expression, body gesture and acoustic analysis [link]

Paper doi abstract bibtex

@article{kessous_multimodal_2010,
	title = {Multimodal emotion recognition in speech-based interaction using facial expression, body gesture and acoustic analysis},
	volume = {3},
	issn = {1783-7677, 1783-8738},
	url = {http://link.springer.com/article/10.1007/s12193-009-0025-5},
	doi = {10.1007/s12193-009-0025-5},
	abstract = {In this paper a study on multimodal automatic emotion recognition during a speech-based interaction is presented. A database was constructed consisting of people pronouncing a sentence in a scenario where they interacted with an agent using speech. Ten people pronounced a sentence corresponding to a command while making 8 different emotional expressions. Gender was equally represented, with speakers of several different native languages including French, German, Greek and Italian. Facial expression, gesture and acoustic analysis of speech were used to extract features relevant to emotion. For the automatic classification of unimodal data, bimodal data and multimodal data, a system based on a Bayesian classifier was used. After performing an automatic classification of each modality, the different modalities were combined using a multimodal approach. Fusion of the modalities at the feature level (before running the classifier) and at the results level (combining results from classifier from each modality) were compared. Fusing the multimodal data resulted in a large increase in the recognition rates in comparison to the unimodal systems: the multimodal approach increased the recognition rate by more than 10\% when compared to the most successful unimodal system. Bimodal emotion recognition based on all combinations of the modalities (i.e., ‘face-gesture’, ‘face-speech’ and ‘gesture-speech’) was also investigated. The results show that the best pairing is ‘gesture-speech’. Using all three modalities resulted in a 3.3\% classification improvement over the best bimodal results.},
	language = {en},
	number = {1-2},
	urldate = {2014-06-02TZ},
	journal = {Journal on Multimodal User Interfaces},
	author = {Kessous, Loic and Castellano, Ginevra and Caridakis, George},
	month = mar,
	year = {2010},
	pages = {33--48}
}

Which words are hard to recognize? Prosodic, lexical, and disfluency factors that increase speech recognition error rates. Goldwater, S., Jurafsky, D., & Manning, C. D Speech Communication, 52(3):181–200, 2010.
doi abstract bibtex

@article{goldwater_which_2010,
	Author = {Goldwater, Sharon and Jurafsky, Dan and Manning, Christopher D},
	Date = {2010},
	Date-Modified = {2018-05-14 07:57:31 +0000},
	Doi = {10.1016/j.specom.2009.10.001},
	Journal = {Speech Communication},
	Keywords = {assessment, disfluencies, interspeaker variation, phonetic knowledge, phonetics, prosody, speech recognition, speech technology},
	Number = {3},
	Pages = {181--200},
	Title = {Which words are hard to recognize? Prosodic, lexical, and disfluency factors that increase speech recognition error rates},
	Volume = {52},
	Year = {2010},
	Abstract = {Despite years of speech recognition research, little is known about which words tend to be misrecognized and why. Previous work has shown that errors increase for infrequent words, short words, and very loud or fast speech, but many other presumed causes of error (e.g., nearby disfluencies, turn-initial words, phonetic neighborhood density) have never been carefully tested. The reasons for the huge differences found in error rates between speakers also remain largely mysterious. Using a mixed-effects regression model, we investigate these and other factors by analyzing the errors of two state-of-the-art recognizers on conversational speech. Words with higher error rates include those with extreme prosodic characteristics, those occurring turn-initially or as discourse markers, and doubly confusable pairs: acoustically similar words that also have similar language model probabilities. Words preceding disfluent interruption points (first repetition tokens and words before fragments) also have higher error rates. Finally, even after accounting for other factors, speaker differences cause enormous variance in error rates, suggesting that speaker error rate variance is not fully explained by differences in word choice, fluency, or prosodic characteristics. We also propose that doubly confusable pairs, rather than high neighborhood density, may better explain phonetic neighborhood errors in human speech processing.},
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Filler words as a speaker classification feature. Ishihara, S. & Kinoshita, Y. In Tabain, M., Fletcher, J., Grayden, D., Hajek, J., & Butcher, A., editors, Proceedings of the 13th Australasian International Conference on Speech Science and Technology, pages 34–37, 2010.

Filler words as a speaker classification feature [pdf]

Paper abstract bibtex

@inproceedings{Ishihara:2010aa,
	Author = {Ishihara, Shunichi and Kinoshita, Yuko},
	Booktitle = {Proceedings of the 13th Australasian International Conference on Speech Science and Technology},
	Date = {2010},
	Date-Added = {2018-04-28 07:53:34 +0000},
	Date-Modified = {2018-05-13 20:08:45 +0000},
	Editor = {Tabain, Marija and Fletcher, Janet and Grayden, David and Hajek, John and Butcher, Andy},
	Eventdate = {2010-12-14/2010-12-16},
	Keywords = {forensic, forensic phonetics, phonetics, speech technology, speaker recognition, disfluencies, pauses, filled pauses, Japanese, spontaneous speech, temporal factors, prosody},
	Location = {Melbourne, Australia},
	Pages = {34--37},
	Title = {Filler words as a speaker classification feature},
	Url = {http://assta.org/sst/SST-10/SST2010/PDF/AUTHOR/ST100050.PDF},
	Year = {2010},
	Abstract = {Many of us can think of iconic words or expressions that our close friends or family often use. This study investigated the potential of idiosyncrasies in usage of filler words as a speaker classification parameter, using spontaneous Japanese speech. We estimated likelihood ratios (LRs) based on which filler words were used and how often, and performed speaker discrimination tests using the LRs. As a result we discovered that speakers’ choices of fillers can predict speaker’s identity to some degree, and that this has potential as an additional feature for speaker classification.},
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	Bdsk-Url-1 = {http://assta.org/sst/SST-10/SST2010/PDF/AUTHOR/ST100050.PDF}}

2009 (4)

Las tecnologías del habla en las lenguas románicas ibéricas. Llisterri, J. Studies in Hispanic and Lusophone Linguistics, 2(1):133-180, 2009.

Las tecnologías del habla en las lenguas románicas ibéricas [pdf]

Paper doi bibtex

@article{llisterri_tecnologias_2009,
	Author = {Llisterri, Joaquim},
	Date = {2009},
	Date-Modified = {2017-11-19 18:43:50 +0000},
	Doi = {10.1515/shll-2009-1040},
	Issn = {1939-0238},
	Journal = {Studies in Hispanic and Lusophone Linguistics},
	Keywords = {Spanish, speech technology},
	Number = {1},
	Pages = {133-180},
	Title = {Las tecnologías del habla en las lenguas románicas ibéricas},
	Url = {http://liceu.uab.cat/~joaquim/publicacions/Llisterri_09_Tecnologias_Habla_Lenguas_Romanicas_Ibericas.pdf},
	Volume = {2},
	Year = {2009},
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	Bdsk-Url-1 = {http://liceu.uab.cat/~joaquim/publicacions/Llisterri_09_Tecnologias_Habla_Lenguas_Romanicas_Ibericas.pdf},
	Bdsk-Url-2 = {http://dx.doi.org/10.1515/shll-2009-1040}}

The Corpus DIMEx100: transcription and evaluation. Pineda, L. A., Castellanos, H., Cuétara, J. O, Galescu, L., Juárez, J., Llisterri, J., Pérez, P., & Villaseñor, L. Language Resources and Evaluation, 44(4):347-370, 2009.

The Corpus DIMEx100: transcription and evaluation [pdf]

Paper doi abstract bibtex

@article{pineda_corpus_2009,
	Author = {Pineda, Luis Alberto and Castellanos, Hayde and Cuétara, Javier O and Galescu, Lucian and Juárez, Janet and Llisterri, Joaquim and Pérez, Patricia and Villaseñor, Luis},
	Date = {2009},
	Date-Modified = {2018-07-21 09:47:32 +0000},
	Doi = {10.1007/s10579-009-9109-9},
	Issn = {1574-020X (Print) 1574-0218 (Online)},
	Journal = {Language Resources and Evaluation},
	Keywords = {geographical variation, language resources, Spanish, speech corpus, speech technology, transcription, América, México, segmental transcription, speech recognition, speech technology},
	Number = {4},
	Pages = {347-370},
	Title = {The Corpus DIMEx100: transcription and evaluation},
	Url = {http://liceu.uab.cat/~joaquim/publicacions/Pineda_et_al_09_DIMEx100.pdf},
	Volume = {44},
	Year = {2009},
	Abstract = {In this paper the transcription and evaluation of the corpus DIMEx100 for Mexican Spanish is presented. First we describe the corpus and explain the linguistic and computational motivation for its design and collection process; then, the phonetic antecedents and the alphabet adopted for the transcription task are presented; the corpus has been transcribed at three different granularity levels, which are also specified in detail. The corpus statistics for each transcription level are also presented. A set of phonetic rules describing phonetic context observed empirically in spontaneous conversation is also validated with the transcription. The corpus has been used for the construction of acoustic models and a phonetic dictionary for the construction of a speech recognition system. Initial performance results suggest that the data can be used to train good quality acoustic models.},
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	Bdsk-Url-1 = {http://liceu.uab.cat/~joaquim/publicacions/Pineda_et_al_09_DIMEx100.pdf},
	Bdsk-Url-2 = {http://dx.doi.org/10.1007/s10579-009-9109-9}}

Les tecnologies de la parla. Llisterri, J. Llengua, Societat i Comunicació. Revista de Sociolingüística de la Universitat de Barcelona, 7:11-19, 2009.

Paper abstract bibtex

@article{llisterri_les_2009,
	Author = {Llisterri, Joaquim},
	Date = {2009},
	Date-Modified = {2017-11-19 18:43:50 +0000},
	File = {Attachment:files/6692/Llisterri - 2009 - Les tecnologies de la parla.pdf:application/pdf},
	Issn = {1697 5928},
	Journal = {Llengua, Societat i Comunicació. Revista de Sociolingüística de la Universitat de Barcelona},
	Keywords = {general, speech technology},
	Language = {ca},
	Pages = {11-19},
	Title = {Les tecnologies de la parla},
	Url = {http://liceu.uab.cat/~joaquim/publicacions/Llisterri_09_Tecnologies_Parla.pdf},
	Volume = {7},
	Year = {2009},
	Abstract = {Les tecnologies de la parla tenen com a objectiu facilitar la interacció entre persones i ordinadors per mitjà de la llengua oral, que és el nostre mitjà de comunicació més natural. Per aquest motiu, són el resultat d'una recerca interdisciplinària en la qual participen ---o haurien de participar--- els lingüistes. En aquest article es pretén mostrar com la conversió de text en parla, el reconeixement automàtic de la parla i els sistemes de diàleg estan assolint un paper cada cop més rellevant tant en la nostra vida quotidiana com en determinats entorns professionals; d'altra banda, s'exposa breument com poden contribuir també a millorar la integració de determinats col·lectius.},
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	Bdsk-Url-1 = {http://liceu.uab.cat/~joaquim/publicacions/Llisterri_09_Tecnologies_Parla.pdf}}

Algoritmo de inserción de pausas para una lengua declinada. Navas, E., Sainz, I., Sanchez, J. A, Saratxaga, I., & Hernáez, I. Procesamiento del Lenguaje Natural, 43:85–92, 2009.

Algoritmo de inserción de pausas para una lengua declinada [link]

Paper abstract bibtex

@article{navas_algoritmo_2009,
	Author = {Navas, Eva and Sainz, Iñaki and Sanchez, Jon A and Saratxaga, Ibon and Hernáez, Inmaculada},
	Date = {2009},
	Date-Modified = {2018-05-14 08:37:59 +0000},
	Journal = {Procesamiento del Lenguaje Natural},
	Keywords = {Basque, disfluencies, pause location, pauses, phonetics, prosody, silent pauses, speech synthesis, speech technology, temporal factors, text-to-speech},
	Pages = {85--92},
	Title = {Algoritmo de inserción de pausas para una lengua declinada},
	Url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/14},
	Volume = {43},
	Year = {2009},
	Abstract = {Los sistemas de conversión de texto en habla necesitan un módulo para insertar las pausas que no vienen indicadas en el texto. En este trabajo se desarrolla un algoritmo de inserción de pausas para euskara estándar que utiliza información morfológica, sintáctica y del caso gramatical. El algoritmo se ha evaluado tanto objetiva como subjetivamente y los resultados confirman que este tipo de información es adecuada para realizar la predicción de las pausas en euskara estándar. Los tres tipos de información son relevantes y contribuyen a la mejora de los resultados del algoritmo.},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/14}}

2007 (5)

Màquines que parlen i que escolten: el paper de la fonètica en el desenvolupament de les tecnologies de la parla. Llisterri, J. In Carrera, J. & Pons, C., editors, Aplicacions de la fonètica. Catorzè Col·loqui Lingüístic de la Universitat de Barcelona, pages 139-169, Barcelona, 2007. Promociones y Publicaciones Universitarias.