RESUMEN
Assistive speech technology is a challenging task because of the impaired nature of dysarthric speech, such as breathy voice, strained speech, distorted vowels, and consonants. Learning compact and discriminative embeddings for dysarthric speech utterances is essential for impaired speech recognition. We propose a Histogram of States (HoS)-based approach that uses Deep Neural Network-Hidden Markov Model (DNN-HMM) to learn word lattice-based compact and discriminative embeddings. Best state sequence chosen from word lattice is used to represent dysarthric speech utterance. A discriminative model-based classifier is then used to recognize these embeddings. The performance of the proposed approach is evaluated using three datasets, namely 15 acoustically similar words, 100-common words datasets of the UA-SPEECH database, and a 50-words dataset of the TORGO database. The proposed HoS-based approach performs significantly better than the traditional Hidden Markov Model and DNN-HMM-based approaches for all three datasets. The discriminative ability and the compactness of the proposed HoS-based embeddings lead to the best accuracy of impaired speech recognition.