ASR for emotional speech: Clarifying the issues and enhancing performance

Authors:
T. Athanaselis;S. Bakamidis;I. Dologlou;R. Cowie;E. Douglas-Cowie;C. Cox
Affiliations:
Department of Speech Technology, Institute for Language and Speech Processing (ILSP), Artemidos 6 & Epidavrou, GR-151 25 Maroussi, Athens, Greece;Department of Speech Technology, Institute for Language and Speech Processing (ILSP), Artemidos 6 & Epidavrou, GR-151 25 Maroussi, Athens, Greece;Department of Speech Technology, Institute for Language and Speech Processing (ILSP), Artemidos 6 & Epidavrou, GR-151 25 Maroussi, Athens, Greece;Department of Psychology, Queen's University, Belfast, UK;Department of Psychology, Queen's University, Belfast, UK;Department of Psychology, Queen's University, Belfast, UK
Venue:
Neural Networks - Special issue: Emotion and brain
Year:
2005

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Abstract

There are multiple reasons to expect that recognising the verbal content of emotional speech will be a difficult problem, and recognition rates reported in the literature are in fact low. Including information about prosody improves recognition rate for emotions simulated by actors, but its relevance to the freer patterns of spontaneous speech is unproven. This paper shows that recognition rate for spontaneous emotionally coloured speech can be improved by using a language model based on increased representation of emotional utterances. The models are derived by adapting an already existing corpus, the British National Corpus (BNC). An emotional lexicon is used to identify emotionally coloured words, and sentences containing these words are recombined with the BNC to form a corpus with a raised proportion of emotional material. Using a language model based on that technique improves recognition rate by about 20%.