Publications
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
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1 - 12 of 12 publications
Automatic Speech Recognition of Conversational Speech in Individuals with Disordered Speech
Bob MacDonald
Rus Heywood
Richard Cave
Katie Seaver
Antoine Desjardins
Jordan Green
Journal of Speech, Language, and Hearing Research (2024) (to appear)
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Purpose: This study examines the effectiveness of automatic speech recognition (ASR) for individuals with speech disorders, addressing the gap in performance between read and conversational ASR. We analyze the factors influencing this disparity and the effect of speech mode-specific training on ASR accuracy.
Method: Recordings of read and conversational speech from 27 individuals with various speech disorders were analyzed using both (1) one speaker-independent ASR system trained and optimized for typical speech and (2) multiple ASR models that were personalized to the speech of the participants with disordered speech. Word Error Rates (WERs) were calculated for each speech mode, read vs conversational, and subject. Linear mixed-effect models were used to assess the impact of speech mode and disorder severity on ASR accuracy. We investigated nine variables, classified as technical, linguistic, or speech impairment factors, for their potential influence on the performance gap.
Results: We found a significant performance gap between read and conversational speech in both personalized and unadapted ASR models. Speech impairment severity notably impacted recognition accuracy in unadapted models for both speech modes and in personalized models for read speech. Linguistic attributes of utterances were the most influential on accuracy, though atypical speech characteristics also played a role. Including conversational speech samples in model training notably improved recognition accuracy.
Conclusions: We observed a significant performance gap in ASR accuracy between read and conversational speech for individuals with speech disorders. This gap was largely due to the linguistic complexity and unique characteristics of speech disorders in conversational speech. Training personalized ASR models using conversational speech significantly improved recognition accuracy, demonstrating the importance of domain-specific training and highlighting the need for further research into ASR systems capable of handling disordered conversational speech effectively.
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Although personalized automatic speech recognition (ASR) models have recently been improved to recognize even severely impaired speech, model performance may degrade over time for persons with degenerating speech. The aims of this study were to (1) analyze the change of performance of ASR over time in individuals with degrading speech, and (2) explore mitigation strategies to optimize recognition throughout disease progression. Speech was recorded by four individuals with degrading speech due to amyotrophic lateral sclerosis (ALS). Word error rates (WER) across recording sessions were computed for three ASR models: Unadapted Speaker Independent (U-SI), Adapted Speaker Independent (A-SI), and Adapted Speaker Dependent (A-SD or personalized). The performance of all models degraded significantly over time as speech became more impaired, but the A-SD model improved markedly when updated with recordings from the severe stages of speech progression. Recording additional utterances early in the disease before significant speech degradation did not improve the performance of A-SD models. This emphasizes the importance of continuous recording (and model retraining) when providing personalized models for individuals with progressive speech impairments.
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Speech Intelligibility Classifiers from 550k Disordered Speech Samples
Katie Seaver
Richard Cave
Neil Zeghidour
Rus Heywood
Jordan Green
ICASSP, Icassp submission. 2022 (2023)
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We developed dysarthric speech intelligibility classifiers on 551,176 disordered speech samples contributed by a diverse set of 468 speakers, with a range of self-reported speaking disorders and rated for their overall intelligibility on a fivepoint scale. We trained three models following different deep learning approaches and evaluated them on ∼94K utterances from 100 speakers. We further found the models to generalize well (without further training) on the TORGO database (100% accuracy), UASpeech (0.93 correlation), ALS-TDI PMP (0.81 AUC) datasets as well as on a dataset of realistic unprompted speech we gathered (106 dysarthric and 76 control speakers, ∼2300 samples).
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This study investigates the performance of personalized automatic speech recognition (ASR) for recognizing disordered speech using small amounts of per-speaker adaptation data. We trained personalized models for 195 individuals with different types and severities of speech impairment with training sets ranging in size from <1 minute to 18-20 minutes of speech data. Word error rate (WER) thresholds were selected to determine success rates (the percentage of personalized models reaching the target WER) in different application scenarios. For the home automation scenario, 79% of speakers reached the target WER with 18-20 minutes of speech; but even with only 3-4 minutes of speech, 63% of speakers reached the target WER. Further evaluation found similar improvement on test sets with out-of-domain, unprompted phrases. Our results demonstrate that with only a few minutes of recordings, individuals with disordered speech could benefit from personalized ASR.
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Assessing ASR Model Quality on Disordered Speech using BERTScore
Qisheng Li
Katie Seaver
Richard Jonathan Noel Cave
Proc. 1st Workshop on Speech for Social Good (S4SG) (2022), pp. 26-30 (to appear)
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Word Error Rate (WER) is the primary metric used to assess automatic speech recognition (ASR) model quality. It has been shown that ASR models tend to have much higher WER on speakers with speech impairments than typical English speakers. It is hard to determine if models can be be useful at such high error rates. This study investigates the use of BERTScore, an evaluation metric for text generation, to provide a more informative measure of ASR model quality and usefulness. Both BERTScore and WER were compared to prediction errors manually annotated by Speech Language Pathologists for error type and assessment. BERTScore was found to be more correlated with human assessment of error type and assessment. BERTScore was specifically more robust to orthographic changes (contraction and normalization errors) where meaning was preserved. Furthermore, BERTScore was a better fit of error assessment than WER, as measured using an ordinal logistic regression and the Akaike's Information Criterion (AIC). Overall, our findings suggest that BERTScore can complement WER when assessing ASR model performance from a practical perspective, especially for accessibility applications where models are useful even at lower accuracy than for typical speech.
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Characterizing Dysarthria Diversity for Automatic Speech Recognition: A Tutorial From the Clinical Perspective
Hannah P. Rowe
Sarah E. Gutz
Marc F. Maffei
Jordan R. Green
Frontiers in Computer Science, 4/2022 (2022)
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Despite significant advancements in automatic speech recognition (ASR) technology, even the best performing ASR systems are inadequate for speakers with impaired speech. This inadequacy may be, in part, due to the challenges associated with acquiring a sufficiently diverse training sample of disordered speech. Speakers with dysarthria, which refers to a group of divergent speech disorders secondary to neurologic injury, exhibit highly variable speech patterns both within and across individuals. This diversity is currently poorly characterized and, consequently, difficult to adequately represent in disordered speech ASR corpora. In this article, we consider the variable expressions of dysarthria within the context of established clinical taxonomies (e.g., Darley, Aronson, and Brown dysarthria subtypes). We also briefly consider past and recent efforts to capture this diversity quantitatively using speech analytics. Understanding dysarthria diversity from the clinical perspective and how this diversity may impact ASR performance could aid in (1) optimizing data collection strategies for minimizing bias; (2) ensuring representative ASR training sets; and (3) improving generalization of ASR for difficult-to-recognize speakers. Our overarching goal is to facilitate the development of robust ASR systems for dysarthric speech using clinical knowledge.
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Comparing Supervised Models And Learned Speech Representations For Classifying Intelligibility Of Disordered Speech On Selected Phrases
Joel Shor
Jordan R. Green
Interspeech, Interspeech 2021 (2021) (to appear)
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Automatic classification of disordered speech can provide an objective tool for identifying the presence and severity of a speech impairment. Classification approaches can also help identify hard-to-recognize speech samples to teach ASR systems about the variable manifestations of impaired speech. Here, we develop and compare different deep learning techniques to classify the intelligibility of disordered speech on selected phrases. We collected samples from a diverse set of 661 speakers with a variety of self-reported disorders speaking 29 words or phrases, which were rated by speech-language pathologists for their overall intelligibility using a five-point Likert scale. We then evaluated classifiers developed using 3 approaches: (1) a convolutional neural network (CNN) trained for the task, (2) classifiers trained on non-semantic speech representations from CNNs that used an unsupervised objective [1], and (3) classifiers trained on the acoustic (encoder) embeddings from an ASR system trained on typical speech [2]. We find that the ASR encoder’s embeddings considerably outperform the other two on detecting and classifying disordered speech. Further analysis shows that the ASR embeddings cluster speech by the spoken phrase, while the non-semantic embeddings cluster speech by speaker. Also, longer phrases are more indicative of intelligibility deficits than single words.
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Disordered Speech Data Collection: Lessons Learned at 1 Million Utterances from Project Euphonia
Bob MacDonald
Rus Heywood
Richard Cave
Katie Seaver
Marilyn Ladewig
Jordan R. Green
Interspeech (2021) (to appear)
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Speech samples from over 1000 individuals with impaired speech have been submitted for Project Euphonia, aimed at improving automated speech recognition for atypical speech. We provide an update on the contents of the corpus, which recently passed 1 million utterances, and review key lessons learned from this project.
The reasoning behind decisions such as phrase set composition, prompted vs extemporaneous speech, metadata and data quality efforts are explained based on findings from both technical and user-facing research.
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Automatic Speech Recognition of Disordered Speech: Personalized models outperforming human listeners on short phrases
Jordan R. Green
Bob MacDonald
Rus Heywood
Richard Cave
Katie Seaver
Marilyn Ladewig
Interspeech (2021) (to appear)
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Objective. This study aimed to (1) evaluate the performance of personalized Automatic Speech Recognition (ASR) models on disordered speech samples representing a wide range of etiologies and speech severities, and (2) compare the accuracy of these models to that of speaker-independent ASR models developed on and for typical speech as well as expert human listeners. Methods. 432 individuals with self-reported disordered speech recorded at least 300 short phrases using a web-based application. Word error rates (WER) were computed using three different ASR models and expert human transcribers. Metadata were collected to evaluate the potential impact of participant, atypical speech, and technical factors on recognition accuracy. Results. The accuracy of personalized models for recognizing disordered speech was high (WER: 4.6%), and significantly better than speaker-independent models (WER: 31%). Personalized models also outperformed human transcribers (WER gain: 9%) with relative gains in accuracy as high as 80%. The most significant gain in recognition performance was for the most severely affected speakers. Low SNR and fewer training utterances adversely affected recognition even for speakers with mild speech impairments. Conclusions. Personalized ASR models have significant potential for improving communication for persons with impaired speech.
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Residual Adapters for Parameter-Efficient ASR Adaptation to Atypical and Accented Speech
Vicky Zayats
Dirk Padfield
Proceedings of EMNLP 2021 (2021)
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Automatic Speech Recognition (ASR) systems are often optimized to work best for speakers with canonical speech patterns. Unfortunately, these systems perform poorly when tested on atypical speech and heavily accented speech. It has previously been shown that personalization through model fine-tuning substantially improves performance. However, maintaining such large models per speaker is costly and difficult to scale. We show that by adding a relatively small number of extra parameters to the encoder layers via so-called residual adapter, we can achieve similar adaptation gains compared to model fine-tuning, while only updating a tiny fraction (less than 0.5%) of the model parameters. We demonstrate this on two speech adaptation tasks (atypical and accented speech) and for two state-of-the-art ASR architectures.
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A Voice-Activated Switch for Persons with Motor and Speech Impairments: Isolated-Vowel Spotting Using Neural Networks
Lisie Lillianfeld
Katie Seaver
Jordan R. Green
InterSpeech 2021 (2021)
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Severe speech impairments limit the precision and range of producible speech sounds. As a result, generic automatic speech recognition (ASR) and keyword spotting (KWS) systems are unable to accurately recognize the utterances produced by individuals with severe speech impairments. This paper describes an approach in which simple speech sounds, namely isolated open vowels (e.g., /a/), are used in lieu of more motorically-demanding keywords. A neural network (NN) is trained to detect these isolated open vowels uttered by individuals with speech impairments against background noise. The NN is trained with a two-phase approach. The pre-training phase uses samples from unimpaired speakers along with samples of background noises and unrelated speech; then the fine-tuning stage uses samples of vowel samples collected from individuals with speech impairments. This model can be built into an experimental mobile app that allows users to activate preconfigured actions such as alerting caregivers. Preliminary user testing indicates the model has the potential to be a useful and flexible emergency communication channel for motor- and speech-impaired individuals.
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Personalizing ASR for Dysarthric and Accented Speech with Limited Data
Joel Shor
Interspeech 2019 (2019)
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Automatic speech recognition (ASR) systems have dramatically
improved over the last few years. ASR systems are most often trained from ‘typical’ speech, which means that underrepresented groups don’t experience the same level of improvement.
In this paper, we present and evaluate finetuning techniques to
improve ASR for users with non standard speech. We focus
on two types of non standard speech: speech from people with
amyotrophic lateral sclerosis (ALS) and accented speech. We
train personalized models that achieve 62% and 35% relative
WER improvement on these two groups, bringing the absolute
WER for ALS speakers, on a test set of message bank phrases,
to 10% for mild dysarthria and 20% for more serious dysarthria.
We show that 76% of the improvement comes from only 5 min
of training data. Finetuning a particular subset of layers (with
many fewer parameters) often gives better results than finetuning the entire model. This is the first step towards building state
of the art ASR models for dysarthric speech
Index Terms: speech recognition, personalization, accessibility
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