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Objective Intelligibility Assessment of Text-to-Speech Systems Through Utterance Verification
Type of publication: Idiap-RR
Citation: Ullmann_Idiap-RR-06-2015
Number: Idiap-RR-06-2015
Year: 2015
Month: 4
Institution: Idiap
Abstract: Objective assessment of synthetic speech intelligibility can be a useful tool for the development of text-to-speech (TTS) systems, as it provides a reproducible and inexpensive alternative to subjective listening tests. In a recent work, it was shown that the intelligibility of synthetic speech could be assessed objectively by comparing two sequences of phoneme class conditional probabilities, corresponding to instances of synthetic and human reference speech, respectively. In this paper, we build on those findings to propose a novel approach that formulates objective intelligibility assessment as an utterance verification problem using hidden Markov models, thereby alleviating the need for human reference speech. Specifically, given each text input to the TTS system, the proposed approach automatically verifies the words in the output synthetic speech signal and estimates an intelligibility score based on word recall statistics. We evaluate the proposed approach on the 2011 Blizzard Challenge data, and show that the estimated scores and the subjective intelligibility scores are highly correlated (Pearson’s |R| = 0.94).
Keywords: KL-divergence, KL-HMM, objective measures, Speech intelligibility, text-to-speech synthesis, utterance verification
Projects ScoreL2
Authors Ullmann, Raphael
Rasipuram, Ramya
Magimai.-Doss, Mathew
Bourlard, Hervé
Crossref by Ullmann_INTERSPEECH_2015
Added by: [ADM]
Total mark: 0
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