Analysis of Head Gesture and Prosody Patterns for Prosody-Driven Head-Gesture Animation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploiting nonacoustic sensors for speech encoding
IEEE Transactions on Audio, Speech, and Language Processing
Adding voice to whisper using a simple heuristic algorithm inferred from empirical observation
ICCHP'10 Proceedings of the 12th international conference on Computers helping people with special needs: Part I
Artificial bandwidth extension of spectral envelope along a Viterbi path
Speech Communication
| Hi-index | 0.00 |
We present a new framework for joint analysis of throat and acoustic microphone (TAM) recordings to improve throat microphone only speech recognition. The proposed analysis framework aims to learn joint sub-phone patterns of throat and acoustic microphone recordings through a parallel branch HMM structure. The joint sub-phone patterns define temporally correlated neighborhoods, in which a linear prediction filter estimates a spectrally rich acoustic feature vector from throat feature vectors. Multimodal speech recognition with throat and throat-driven acoustic features significantly improves throat-only speech recognition performance. Experimental evaluations on a parallel TAM database yield benchmark phoneme recognition rates for throat-only and multimodal TAM speech recognition systems as 46.81% and 60.69%, respectively. The proposed throat-driven multimodal speech recognition system improves phoneme recognition rate to 52.58%, a significant relative improvement with respect to the throat-only speech recognition benchmark system.