Speaker similarity evaluation of foreign-accented speech synthesis using HMM-based speaker adaptation

This paper describes a speaker discrimination experiment in which native English listeners were presented with natural and synthetic speech stimuli in English and were asked to judge whether they thought the sentences were spoken by the same person or not. The natural speech consisted of recordings of Finnish speakers speaking English. The synthetic stimuli were created using adaptation data from the same Finnish speakers. Two average voice models were compared: one trained on Finnish-accented English and the other on American-accented English. The experiments illustrate that listeners perform well at speaker discrimination when the stimuli are both natural or both synthetic, but when the speech types are crossed performance drops significantly. We also found that the type of accent in the average voice model had no effect on the listeners' speaker discrimination performance

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

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

Using stacked transformations for recognizing foreign accented speech

A common problem in speech recognition for foreign accented speech is that there is not enough training data for an accent-specific or a speaker-specific recognizer. Speaker adaptation can be used to improve the accuracy of a speaker independent recognizer, but a lot of adaptation data is needed for speakers with a strong foreign accent. In this paper we propose a rather simple and successful technique of stacked transformations where the baseline models trained for native speakers are first adapted by using accent-specific data and then by another transformation using speaker-specific data. Because the accent-specific data can be collected offline, the first transformation can be more detailed and comprehensive, and the second one less detailed and fast. Experimental results are provided for speaker adaptation in English spoken by Finnish speakers. The evaluation results confirm that the stacked transformations are very helpful for fast speaker adaptation.Peer reviewe

Articulatory-WaveNet: Deep Autoregressive Model for Acoustic-to-Articulatory Inversion

Acoustic-to-Articulatory Inversion, the estimation of articulatory kinematics from speech, is an important problem which has received significant attention in recent years. Estimated articulatory movements from such models can be used for many applications, including speech synthesis, automatic speech recognition, and facial kinematics for talking-head animation devices. Knowledge about the position of the articulators can also be extremely useful in speech therapy systems and Computer-Aided Language Learning (CALL) and Computer-Aided Pronunciation Training (CAPT) systems for second language learners. Acoustic-to-Articulatory Inversion is a challenging problem due to the complexity of articulation patterns and significant inter-speaker differences. This is even more challenging when applied to non-native speakers without any kinematic training data. This dissertation attempts to address these problems through the development of up-graded architectures for Articulatory Inversion. The proposed Articulatory-WaveNet architecture is based on a dilated causal convolutional layer structure that improves the Acoustic-to-Articulatory Inversion estimated results for both speaker-dependent and speaker-independent scenarios. The system has been evaluated on the ElectroMagnetic Articulography corpus of Mandarin Accented English (EMA-MAE) corpus, consisting of 39 speakers including both native English speakers and Mandarin accented English speakers. Results show that Articulatory-WaveNet improves the performance of the speaker-dependent and speaker-independent Acoustic-to-Articulatory Inversion systems significantly compared to the previously reported results

Stacked transformations for foreign accented speech recognition

Nowadays, large vocabulary speech recognizers exist that are performing reasonably well for specific conditions and environments. When the conditions change however, performance degrades quickly. For example, when the person to be recognized has a foreign accent the conditions could mismatch with the model, resulting in high error rates. The problem in recognizing foreign accented speech is the lack of sufficient training data. If enough data would be available of the same accent, from numerous different speakers, a well performing accented speech model could be built. Besides the lack of speech data, there are more problems with training a complete new model. It costs a lot of computational resources and storage space to train a new model. If speakers with different accents must be recognized, these costs explode as every accent needs retraining. A common solution for preventing retraining is to adapt (transform) an existing model, such that it better matches the recognition conditions. In this thesis multiple different adaptation transformations are considered. Speaker Transformations are using speech data from the target speaker, Accent Transformations use speech data from different speakers, who have the same accent as the speech that needs to be recognized. Neighbour Transformations are estimated with speech from different speakers that are automatically determined to be similar to the target speaker. Novelty in this work is the stack wise combination of these adaptations. Instead of using a single transformation, multiple transformations are 'stacked together'. Because all adaptations except the speaker specific adaptation can be precomputed, no extra computational costs at recognition time occur compared to normal speaker adaptation and the adaptations that can be precomputed are much more refined as they can use more and better adaptation data. In addition, they need only a very small amount storage space, compared to a retrained model. The effect of Stacked Transformations is that the models have a better fit for the recognition utterances. When compared to no adaptation, improvements up to 30% in Word Error Rate can be achieved. In adaptation with a small number (5) of sentences, improvements up to 15% are gained

Speaker Independent Acoustic-to-Articulatory Inversion

Acoustic-to-articulatory inversion, the determination of articulatory parameters from acoustic signals, is a difficult but important problem for many speech processing applications, such as automatic speech recognition (ASR) and computer aided pronunciation training (CAPT). In recent years, several approaches have been successfully implemented for speaker dependent models with parallel acoustic and kinematic training data. However, in many practical applications inversion is needed for new speakers for whom no articulatory data is available. In order to address this problem, this dissertation introduces a novel speaker adaptation approach called Parallel Reference Speaker Weighting (PRSW), based on parallel acoustic and articulatory Hidden Markov Models (HMM). This approach uses a robust normalized articulatory space and palate referenced articulatory features combined with speaker-weighted adaptation to form an inversion mapping for new speakers that can accurately estimate articulatory trajectories. The proposed PRSW method is evaluated on the newly collected Marquette electromagnetic articulography - Mandarin Accented English (EMA-MAE) corpus using 20 native English speakers. Cross-speaker inversion results show that given a good selection of reference speakers with consistent acoustic and articulatory patterns, the PRSW approach gives good speaker independent inversion performance even without kinematic training data

Intonation Modelling for Speech Synthesis and Emphasis Preservation

Speech-to-speech translation is a framework which recognises speech in an input language, translates it to a target language and synthesises speech in this target language. In such a system, variations in the speech signal which are inherent to natural human speech are lost, as the information goes through the different building blocks of the translation process. The work presented in this thesis addresses aspects of speech synthesis which are lost in traditional speech-to-speech translation approaches. The main research axis of this thesis is the study of prosody for speech synthesis and emphasis preservation. A first investigation of regional accents of spoken French is carried out to understand the sensitivity of native listeners with respect to accented speech synthesis. Listening tests show that standard adaptation methods for speech synthesis are not sufficient for listeners to perceive accentedness. On the other hand, combining adaptation with original prosody allows perception of accents. Addressing the need of a more suitable prosody model, a physiologically plausible intonation model is proposed. Inspired by the command-response model, it has basic components, which can be related to muscle responses to nerve impulses. These components are assumed to be a representation of muscle control of the vocal folds. A motivation for such a model is its theoretical language independence, based on the fact that humans share the same vocal apparatus. An automatic parameter extraction method which integrates a perceptually relevant measure is proposed with the model. This approach is evaluated and compared with the standard command-response model. Two corpora including sentences with emphasised words are presented, in the context of the SIWIS project. The first is a multilingual corpus with speech from multiple speaker; the second is a high quality speech synthesis oriented corpus from a professional speaker. Two broad uses of the model are evaluated. The first shows that it is difficult to predict model parameters; however the second shows that parameters can be transferred in the context of emphasis synthesis. A relation between model parameters and linguistic features such as stress and accent is demonstrated. Similar observations are made between the parameters and emphasis. Following, we investigate the extraction of atoms in emphasised speech and their transfer in neutral speech, which turns out to elicit emphasis perception. Using clustering methods, this is extended to the emphasis of other words, using linguistic context. This approach is validated by listening tests, in the case of English

Data-Driven Enhancement of State Mapping-Based Cross-Lingual Speaker Adaptation

The thesis work was motivated by the goal of developing personalized speech-to-speech translation and focused on one of its key component techniques – cross-lingual speaker adaptation for text-to-speech synthesis. A personalized speech-to-speech translator enables a person’s spoken input to be translated into spoken output in another language while maintaining his/her voice identity. Before addressing any technical issues, work in this thesis set out to understand human perception of speaker identity. Listening tests were conducted in order to determine whether people could differentiate between speakers when they spoke different languages. The results demonstrated that differentiating between speakers across languages was an achievable task. However, it was difficult for listeners to differentiate between speakers across both languages and speech types (original recordings versus synthesized samples). The underlying challenge in cross-lingual speaker adaptation is how to apply speaker adaptation techniques when the language of adaptation data is different from that of synthesis models. The main body of the thesis work was devoted to the analysis and improvement of HMM state mapping-based cross-lingual speaker adaptation. Firstly, the effect of unsupervised cross-lingual adaptation was investigated, as it relates to the application scenario of personalized speech-to-speech translation. The comparison of paired supervised and unsupervised systems shows that the performance of unsupervised cross-lingual speaker adaptation is comparable to that of the supervised fashion, even if the average phoneme error rate of the unsupervised systems is around 75%. Then the effect of the language mismatch between synthesis models and adaptation data was investigated. The mismatch is found to transfer undesirable language information from adaptation data to synthesis models, thereby limiting the effectiveness of generating multiple regression class-specific transforms, using larger quantities of adaptation data and estimating adaptation transforms iteratively. Thirdly, in order to tackle the problems caused by the language mismatch, a data-driven adaptation framework using phonological knowledge is proposed. Its basic idea is to group HMM states according to phonological knowledge in a data-driven manner and then to map each state to a phonologically consistent counterpart in a different language. This framework is also applied to regression class tree construction for transform estimation. It is found that the proposed framework alleviates the negative effect of the language mismatch and gives consistent improvement compared to previous state-of-the-art approaches. Finally, a two-layer hierarchical transformation framework is developed, where one layer captures speaker characteristics and the other compensates for the language mismatch. The most appropriate means to construct the hierarchical arrangement of transforms was investigated in an initial study. While early results show some promise, further in-depth investigation is needed to confirm the validity of this hierarchy