Cross-Lingual Speaker Discrimination Using Natural and Synthetic Speech

This paper describes speaker discrimination experiments in which native English listeners were presented with either natural speech stimuli in English and Mandarin, synthetic speech stimuli in English and Mandarin, or natural Mandarin speech and synthetic English speech stimuli. In each experiment, listeners were asked to decide whether they thought the sentences were spoken by the same person or not. We found that the results for Mandarin/English speaker discrimination are very similar to results found in previous work on German/English and Finnish/English speaker discrimination. We conclude from this and previous work that listeners are able to identify speakers across languages and they are able to identify speakers across speech types, but the combination of these two factors leads to a speaker discrimination task which is too difficult for listeners to perform successfully, given the quality of across-language speaker adapted speech synthesis at present. Index Terms: speaker discrimination, speaker adaptation, HMM-based speech synthesi

Cross-lingual talker discrimination

This paper describes a talker discrimination experiment in which native English listeners were presented with two sentences spoken by bilingual talkers (English/German and English/Finnish) and were asked to judge whether they thought the sentences were spoken by the same person or not. Equal amounts of cross-lingual and matched-language trials were presented. The experiments showed that listeners are able to complete this task well, they can discriminate between talkers significantly better than chance. However, listeners are significantly less accurate on cross-lingual talker trials than on matched-language pairs. No significant differences were found on this task between German and Finnish. Bias (B'') and Sensitivity (A') values are presented to analyse the listeners' behaviour in more detail. The results are promising for the evaluation of EMIME, a project covering speech-to-speech translation with speaker adaptation

Rapid Adaptation of Foreign-accented HMM-based Speech Synthesis

This paper presents findings of listeners ’ perception of speaker identity in synthetic speech. Specifically, we investigated what the effect is on the perceived identity of a speaker when using differently accented average voice models and limited amounts (five and fifteen sentences) of a speaker’s data to create the synthetic stimuli. A speaker discrimination task was used to measure speaker identity. Native English listeners were presented with natural and synthetic speech stimuli in English and were asked to decide whether they thought the sentences were spoken by the same person or not. An accent rating task was also carried out to measure the perceived accents of the synthetic speech stimuli. The results show that listeners, for the most part, perform as well at speaker discrimination when the stimuli have been created using five or fifteen adaptation sentences as when using 105 sentences. Furthermore, the accent of the average voice model does not affect listeners ’ speaker discrimination performance even though the accent rating task shows listeners are perceiving different accents in the synthetic stimuli. Listeners do not base their speaker similarity decisions on perceived accent. Index Terms: speech synthesis, rapid adaptation 1

Pronunciation modeling for ASR - knowledge-based and data-derived methods.

This article focuses on modeling pronunciation variation in two different ways: data-derived and knowledge-based. The knowledge-based approach consists of using phonological rules to generate variants. The data-derived approach consists of performing phone recognition, followed by smoothing using decision trees (D-trees) to alleviate some of the errors in the phone recognition. Using phonological rules led to a small improvement in WER; a data-derived approach in which the phone recognition was smoothed using D-trees prior to lexicon generation led to larger improvements compared to the baseline. The lexicon was employed in two different recognition systems: a hybrid HMM/ANN system and a HMM-based system, to ascertain whether pronunciation variation was truly being modeled. This proved to be the case as no significant differences were found between the results obtained with the two systems. Furthermore, we found that 10% of variants generated by the phonological rules were also found using phone recognition, and this increased to 28% when the phone recognition output was smoothed by using D-trees. This indicates that the D-trees generalize beyond what has been seen in the training material, whereas when the phone recognition approach is employed directly, unseen pronunciations cannot be predicted. In addition, we propose a metric to measure confusability in the lexicon. Using this confusion metric to prune variants results in roughly the same improvement as using the D-tree method

The EMIME Bilingual Database

This paper describes the collection of a bilingual database of Finnish/English and German/English data. In addition, the accents of the talkers in the database have been rated. English, German and Finnish listeners assessed the English, German and Finnish talkers' degree of foreign accent in English. Native English listeners showed higher inter-listener agreement than non-native listeners. Further analyses showed that non-native listeners judged Finnish and German female talkers to be significantly less accented than do English listeners. German males are judged less accented by Finnish listeners than they are by English and German listeners and there is no difference between listeners as to how they judge the accent of Finnish males. Finally, all English talkers are judged more accented by non-native listeners than they are by native English listeners

Syllable Classification Using Articulatory-Acoustic Features

This paper investigates the use of articulatory-acoustic features for the classification of syllables in TIMIT. The main motivation for this study is to circumvent the "beads-on-a-string" problem, i.e. the assumption that words can be described as a simple concatenation of phones. Posterior probabilities for articulatory-acoustic features are obtained from artificial neural nets and are used to classify speech within the scope of syllables instead of phones. This gives the opportunity to account for asynchronous feature changes, exploiting the strengths of the articulatory-acoustic features, instead of losing the potential by reverting to phones

Automatic Classification of Voice Quality: Comparing Regression Models and Hidden Markov Models

In this paper, two methods for automatically classifying voice quality are compared: regression analysis and hidden Markov models (HMMs). The findings of this research show that HMMs can be used to classify voice quality. The HMMs performed better than the regression models in classifying breathiness and overall degree of deviance, and the two methods showed similar results on the roughness scale. However, the results are not spectacular. This is mainly due to the type of material that was available and the number of listeners who assessed the material. Nonetheless, I argue in this paper that these findings are interesting because they are a promising step towards developing a system for classifying voice quality

Towards Hierarchical Prosodic Prominence Generation in TTS Synthesis

We address the problem of identification (from text) and generation of pitch accents in HMM-based English TTS synthesis. We show, through a large scale perceptual test, that a large improvement of the binary discrimination between pitch accented and non-accented words has no effect on the quality of the speech generated by the system. On the other side adding a third accent type that emphatically marks words that convey ”contrastive” focus (automatically identified from text) produces beneficial effects on the synthesized speech. These results support the accounts on prosodic prominence that consider the prosodic patterns of utterances as hierarchical structured and point out the limits of a flattening of such structure resulting from a simple accent/non-accent distinction. Index Terms: speech synthesis, HMM, pitch accents, focus detection 1