Detection of accents, phrase boundaries, and sentence modality in German

In this paper detectors for accents, phrase boundaries, and sentence modality are described which derive prosodic features only from the speech signal and its fundamental frequency to support other modules of a speech understanding system in an early analysis stage, or in cases where no word hypotheses are available. A new method for interpolating and decomposing the fundamental frequency is suggested. The detectors\u27 underlying Gaussian distribution classifiers were trained and tested with approximately 50 minutes of spontaneous speech, yielding recognition rates of 78 percent for accents, 80 percent for phrase boundaries, and 85 percent for sentence modality

Including Pitch Accent Optionality in Unit Selection Text-to-Speech Synthesis

A significant variability in pitch accent placement is found when comparing the patterns of prosodic prominence realized by different English speakers reading the same sentences. In this paper we describe a simple approach to incorporate this variability to synthesize prosodic prominence in unit selection text-to-speech synthesis. The main motivation of our approach is that by taking into account the variability of accent placements we enlarge the set of prosodically acceptable speech units, thus increasing the chances of selecting a good quality sequence of units, both in prosodic and segmental terms. Results on a large scale perceptual test show the benefits of our approach and indicate directions for further improvements. Index Terms: speech synthesis, unit selection, prosodic prominence, pitch accent

What´s in the "pure" prosody?

Detectors for accents and phrase boundaries have been developed which derive prosodic features from the speech signal and its fundamental frequency to support other modules of a speech understanding system in an early analysis stage, or in cases where no word hypotheses are available.The detectors underlying Gaussian distribution classifiers were trained with 50 minutes and tested with 30 minutes of spontaneous speech, yielding recognition rates of 74% for accents and 86% for phrase boundaries. Since this material was prosodically hand labelled, the question was, which labels for phrase boundaries and accentuation were only guided by syntactic or semantic knowledge, and which ones are really prosodically marked.Therefore a small test subset has been resynthesized in such a way that comprehensibility was lost, but the prosodic characteristics were kept. This subset has been re-labelled by 11listeners with nearly the same accuracy as the detectors

A classifier-based target cost for unit selection speech synthesis trained on perceptual data

Our goal is to automatically learn a PERCEPTUALLY-optimal target cost function for a unit selection speech synthesiser. The approach we take here is to train a classifier on human perceptual judgements of synthetic speech. The output of the classifier is used to make a simple three-way distinction rather than to estimate a continuously-valued cost. In order to collect the necessary perceptual data, we synthesised 145,137 short sentences with the usual target cost switched off, so that the search was driven by the join cost only. We then selected the 7200 sentences with the best joins and asked 60 listeners to judge them, providing their ratings for each syllable. From this, we derived a rating for each demiphone. Using as input the same context features employed in our conventional target cost function, we trained a classifier on these human perceptual ratings. We synthesised two sets of test sentences with both our standard target cost and the new target cost based on the classifier. A/B preference tests showed that the classifier-based target cost, which was learned completely automatically from modest amounts of perceptual data, is almost as good as our carefully- and expertly-tuned standard target cost

Optimizing Phonetic Encoding for Viennese Unit Selection Speech Synthesis

While developing lexical resources for a particular language variety (Viennese), we experimented with a set of 5 different phonetic encodings, termed phone sets, used for unit selection speech synthesis. We started with a very rich phone set based on phonological considerations and covering as much phonetic variability as possible, which was then reduced to smaller sets by applying transformation rules that map or merge phone symbols. The optimal trade-off was found measuring the phone error rates of automatically learnt grapheme-to-phone rules and by a perceptual evaluation of 27 representative synthesized sentences. Further, we describe a method to semi-automatically enlarge the lexical resources for the target language variety using a lexicon base for Standard Austrian German

From text to prosody without ToBI

A new method for predicting prosodic parameters, i.e. phone durations and F0 targets, from preprocessed text is presented. The prosody model comprises a set of CARTs, which are learned from a large database of labeled speech. This database need not be annotated with Tone and Break Indices (ToBI labels). Instead, a simpler symbolic prosodic description is created by a bootstrapping method. The method had been applied to one Spanish and two German speakers. For the German voices, two listening tests showed a significant preference for the new method over a more traditional approach of prosody prediction, based on hand-crafted rules

What's in the

Detectors for accents and phrase boundaries have been developed which derive prosodic features from the speech signal and its fundamental frequency to support other modules of a speech understanding system in an early analysis stage, or in cases where no word hypotheses are available. The detectors' underlying Gaussian distribution classifiers were trained with 50 minutes and tested with 30 minutes of spontaneous speech, yielding recognition rates of 74% for accents and 86% for phrase boundaries. Since this material was prosodically hand labelled, the question was: which labels for phrase boundaries and accentuation were only guided by syntactic or semantic knowledge, and which ones are really prosodically marked? Therefore a small test subset has been resynthesized in such a way that comprehensibility was lost, but the prosodic characteristics were kept. This subset has been relabelled by 11 listeners with nearly the same accuracy as the detector

Prosodic modules for speech recognition and understanding in VERBMOBIL

Within VERBMOBIL, a large project on spoken language research in Germany, two modules for detecting and recognizing prosodic events have been developed. One module operates on speech signal parameters and the word hypothesis graph, whereas the other module, designed for a novel, highly interactive architecture, only uses speech signal parameters as its input. Phrase boundaries, sentence modality, and accents are detected. The recognition rates in spontaneous dialogs are for accents up to 82,5%, for phrase boundaries up to 91,7%