Signal and Acoustic Modeling for Speech and Communication Disorders demonstrates how speech signal processing and acoustic modeling can be instrumental in early detection and successful intervention with speech deficits resulting from Parkinson's disease, Autism Spectrum disorder, cleft palate, intellectual disabilities, and neuro-motor impairments. Utilizing some of the most advanced methods in signal and acoustic modeling, this eminent group of contributors show how such technologies can inure to the benefit of healthcare and to society writ large. Paradoxically, what most of us take for granted still remains a Sisyphean battle for those with speech and language disorders, who struggle every day to make themselves heard and understood. The purpose of this book is to stimulate a vibrant discussion among speech scientists, system designers, and practitioners on how to best marshal the latest advances in signal and acoustic modeling to address some of the most challenging speech and communication disorders affecting a wide variety of patient populations across the world.

Dialect Accent Features for Establishing Speaker Identity: A Case Study discusses the subject of forensic voice identification and speaker profiling. Specifically focusing on speaker profiling and using dialects of the Hindi language, widely used in India, the authors have contributed to the body of research on speaker identification by using accent feature as the discriminating factor. This case study contributes to the understanding of the speaker identification process in a situation where unknown speech samples are in different language/dialect than the recording of a suspect. The authors' data establishes that vowel quality, quantity, intonation and tone of a speaker as compared to Khariboli (standard Hindi) could be the potential features for identification of dialect accent.

Dialect Accent Features for Establishing Speaker Identity: A Case Study discusses the subject of forensic voice identification and speaker profiling. Specifically focusing on speaker profiling and using dialects of the Hindi language, widely used in India, the authors have contributed to the body of research on speaker identification by using accent feature as the discriminating factor. This case study contributes to the understanding of the speaker identification process in a situation where unknown speech samples are in different language/dialect than the recording of a suspect. The authors' data establishes that vowel quality, quantity, intonation and tone of a speaker as compared to Khariboli (standard Hindi) could be the potential features for identification of dialect accent.