Dysarthric speech analysis and automatic recognition using phase based representations

Dysarthria is a neurological speech impairment which usually results in the loss of motor speech control due to muscular atrophy and poor coordination of articulators. Dysarthric speech is more difficult to model with machine learning algorithms, due to inconsistencies in the acoustic signal and to limited amounts of training data. This study reports a new approach for the analysis and representation of dysarthric speech, and applies it to improve ASR performance. The Zeros of Z-Transform (ZZT) are investigated for dysarthric vowel segments. It shows evidence of a phase-based acoustic phenomenon that is responsible for the way the distribution of zero patterns relate to speech intelligibility. It is investigated whether such phase-based artefacts can be systematically exploited to understand their association with intelligibility. A metric based on the phase slope deviation (PSD) is introduced that are observed in the unwrapped phase spectrum of dysarthric vowel segments. The metric compares the differences between the slopes of dysarthric vowels and typical vowels. The PSD shows a strong and nearly linear correspondence with the intelligibility of the speaker, and it is shown to hold for two separate databases of dysarthric speakers. A systematic procedure for correcting the underlying phase deviations results in a significant improvement in ASR performance for speakers with severe and moderate dysarthria. In addition, information encoded in the phase component of the Fourier transform of dysarthric speech is exploited in the group delay spectrum. Its properties are found to represent disordered speech more effectively than the magnitude spectrum. Dysarthric ASR performance was significantly improved using phase-based cepstral features in comparison to the conventional MFCCs. A combined approach utilising the benefits of PSD corrections and phase-based features was found to surpass all the previous performance on the UASPEECH database of dysarthric speech

A qualitative study exploring the effect of communicating with partially intelligible speech

Few studies have investigated how individuals with partially intelligible speech choose to communicate, including how, when, and why they might use a speech-generating device (SGD). This study aimed to add to the literature by exploring how this group of individuals use different communication strategies. Qualitative interviews were carried out with 10 participants with partially intelligible speech with the aim of investigating participants’ perceptions of modes of communication and communication strategies. Transcripts were analyzed using Framework Analysis to investigate the role of SGDs alongside other communication strategies. Factors that influence why, when, and how a person chooses to communicate were identified and these were interpreted as an explanatory model of communication with partially intelligible speech. Participants described how they made the decision whether to attempt to communicate at all and then which communication method to use. Decision-making was influenced by the importance of the message, how much time is available, past experience, and the communication partner. Each communication attempt adds to an individuals’ experience of communicating and influences subsequent decisions. This study suggests that individuals with partially intelligible speech are at risk of reduced communication environments and networks and that current SGDs may not be designed in a way that recognizes their particular needs

Multimodal Based Audio-Visual Speech Recognition for Hard-of-Hearing: State of the Art Techniques and Challenges

Multimodal Integration (MI) is the study of merging the knowledge acquired by the nervous system using sensory modalities such as speech, vision, touch, and gesture. The applications of MI expand over the areas of Audio-Visual Speech Recognition (AVSR), Sign Language Recognition (SLR), Emotion Recognition (ER), Bio Metrics Applications (BMA), Affect Recognition (AR), Multimedia Retrieval (MR), etc. The fusion of modalities such as hand gestures- facial, lip- hand position, etc., are mainly used sensory modalities for the development of hearing-impaired multimodal systems. This paper encapsulates an overview of multimodal systems available within literature towards hearing impaired studies. This paper also discusses some of the studies related to hearing-impaired acoustic analysis. It is observed that very less algorithms have been developed for hearing impaired AVSR as compared to normal hearing. Thus, the study of audio-visual based speech recognition systems for the hearing impaired is highly demanded for the people who are trying to communicate with natively speaking languages.  This paper also highlights the state-of-the-art techniques in AVSR and the challenges faced by the researchers for the development of AVSR systems