The Nemours database is a collection of 814 short nonsense sentences; 74 sentences spoken by each of 11 male speakers with varying degrees of dysarthria. Additionally, the database contains two connected-speech paragraphs produced by each of the 11 speakers. The database was designed to test the intelligibility of dysarthric speech before and after enhancement by various signal processing methods, and is available on CD-ROM. It can also be used to investigate general characteristics of dysarthric speech such as production error patterns. The entire database has been marked at the word level and sentences for 10 of the 11 talkers have been marked at the phoneme level as well. The paper describes the database structure and techniques adopted to improve the performance of a Discrete Hidden Markov Model (DHMM) labeler used to assign initial phoneme labels to the elements of the database. These techniques may be useful in the design of automatic recognition systems for persons with speech disorders, especially when limited amounts of training data are available.

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The Nemours database of dysarthric speech

Tools and Technologies for Computer-Aided Speech and Language Therapy

A speech-controlled environmental control system for people with severe dysarthria.

We describe an unusual ASR application: recognition of command words from severely dysarthric speakers, who have poor control of their articulators. The goal is to allow these clients to control assistive technology by voice. While this is a small vocabulary, speaker-dependent, isolated-word application, the speech material is more variable than normal, and only a small amount of data is available for training. After training a CDHMM recogniser, it is necessary to predict its likely performance without using an independent test set,so that confusable words can be replaced by alternatives. We present a battery of measures of consistency and confusability, based on forced-alignment, which can be used to predict recogniser performance. We show how these measures perform, and how they are presented to the clinicians who are the users of the system.

/pdf/automatic-speech-recognition-with-sparse-training-data-for-2aafganqt4.pdf

Automatic speech recognition with sparse training data for dysarthric speakers.

Each year thousands of individuals require surgical removal of the larynx (voice box) due to trauma or disease, and thereby require an alternative voice source or assistive device to verbally communicate. Although natural voice is lost after laryngectomy, most muscles controlling speech articulation remain intact. Surface electromyographic (sEMG) activity of speech musculature can be recorded from the neck and face, and used for automatic speech recognition to provide speech-to-text or synthesized speech as an alternative means of communication. This is true even when speech is mouthed or spoken in a silent (subvocal) manner, making it an appropriate communication platform after laryngectomy. In this study, eight individuals at least 6 months after total laryngectomy were recorded using eight sEMG sensors on their face (4) and neck (4) while reading phrases constructed from a 2500-word vocabulary. A unique set of phrases were used for training phoneme-based recognition models for each of the 39 commonly used phonemes in English, and the remaining phrases were used for testing word recognition of the models based on phoneme identification from running speech. Word error rates were on average 10.3% for the full eight-sensor set (averaging 9.5% for the top four participants), and 13.6% when reducing the sensor set to four locations per individual (n = 7). This study provides a compelling proof-of-concept for sEMG-based alaryngeal speech recognition, with the strong potential to further improve recognition performance.

/pdf/silent-speech-recognition-as-an-alternative-communication-2vem4pvcwz.pdf

J.R. Deller

Papers

On the use of hidden Markov modelling for recognition of Dysarthric speech