Development of sEMG sensors and algorithms for silent speech recognition.

OBJECTIVE: Speech is among the most natural forms of human communication, thereby offering an attractive modality for human-machine interaction through automatic speech recognition (ASR). However, the limitations of ASR-including degradation in the presence of ambient noise, limited privacy and poor accessibility for those with significant speech disorders-have motivated the need for alternative non-acoustic modalities of subvocal or silent speech recognition (SSR). APPROACH: We have developed a new system of face- and neck-worn sensors and signal processing algorithms that are capable of recognizing silently mouthed words and phrases entirely from the surface electromyographic (sEMG) signals recorded from muscles of the face and neck that are involved in the production of speech. The algorithms were strategically developed by evolving speech recognition models: first for recognizing isolated words by extracting speech-related features from sEMG signals, then for recognizing sequences of words from patterns of sEMG signals using grammar models, and finally for recognizing a vocabulary of previously untrained words using phoneme-based models. The final recognition algorithms were integrated with specially designed multi-point, miniaturized sensors that can be arranged in flexible geometries to record high-fidelity sEMG signal measurements from small articulator muscles of the face and neck. MAIN RESULTS: We tested the system of sensors and algorithms during a series of subvocal speech experiments involving more than 1200 phrases generated from a 2200-word vocabulary and achieved an 8.9%-word error rate (91.1% recognition rate), far surpassing previous attempts in the field. SIGNIFICANCE: These results demonstrate the viability of our system as an alternative modality of communication for a multitude of applications including: persons with speech impairments following a laryngectomy; military personnel requiring hands-free covert communication; or the consumer in need of privacy while speaking on a mobile phone in public.

Silent Speech Recognition as an Alternative Communication Device for Persons with Laryngectomy.

Each year thousands of individuals require surgical removal of their 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, 8 individuals at least 6 months after total laryngectomy were recorded using 8 sEMG sensors on their face (4) and neck (4) while reading phrases constructed from a 2,500-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 8-sensor set (averaging 9.5% for the top 4 participants), and 13.6% when reducing the sensor set to 4 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.

Signal acquisition and processing techniques for sEMG based silent speech recognition.

sEMG based silent speech recognition systems seek to bypass the limitations of acoustic speech recognition by measuring and interpreting muscle activity of the facial and neck musculature involved in speech production. However, this speech recognition modality introduces unique challenges of its own. This paper describes signal acquisition and processing strategies that we have employed to address these challenges during our development of a silent speech recognition system.