In this paper, an automatic and unsupervised method using context-dependent hiddenMarkov models (CD-HMMs) is proposed for the prosodic labeling of speech synthesis databases. Collectively, our findings support the notion of prosodic phrases that represent coherent patterns across textual and acoustic parameters. The resulting phrases preserve syntactic validity, exhibit pitch reset, and compare well with manual tagging of prosodic boundaries. Boundaries are identified using discontinuities in speech rate (pre-boundary lengthening and phrase-initial acceleration) and silent pauses. We propose a method which does not require model training and utilizes two prosodic cues that are based on ASR output. Efforts to date have focused on detecting phrase boundaries using a variety of linguistic and acoustic cues. ![]() This is done naturally by the human ear, yet has proved surprisingly difficult to achieve reliably and simply in an automatic manner. The ability to parse conversational speech depends crucially on the ability to identify boundaries between prosodic phrases. Based on the feature analysis, we also verify some linguistic conclusions.Īutomatic speech recognition (ASR) and natural language processing (NLP) are expected to benefit from an effective, simple, and reliable method to automatically parse conversational speech. The functions of different features, such as duration, pitch, energy, and intensity, are analyzed and compared in Mandarin and English prosodic break detection. The other is the feature analysis for prosodic break detection. Our proposed method achieves better performance on both the Mandarin prosodic annotation corpus - Annotated Speech Corpus of Chinese Discourse and the English prosodic annotation corpus - Boston University Radio News Corpus when compared with the baseline system and other researches' experimental results. ![]() One is that we use classifier combination method to detect Mandarin and English prosodic break using acoustic, lexical and syntactic evidence. The contributions of the paper are two aspects. In this paper, we discuss automatic prosodic break detection and feature analysis. Automatic prosodic break detection and annotation are important for both speech understanding and natural speech synthesis.
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