236 research outputs found
Integrated approaches to prosodic word prediction for Chinese TTS
We focus on integrated prosodic word prediction for Chinese TTS. To avoid the problem of inconsistency between lexical words and prosodic words in Chinese, lexical word segmentation and prosodic word prediction are taken as one process instead of two independent tasks. Furthermore, two word-based approaches are proposed to drive this integrated prosodic word prediction: The first one follows the notion of lexicalized hidden Markov models, and the second one is borrowed from unknown word identification for Chinese. The results of our primary experiment show these integrated approaches are effective.published_or_final_versio
Improving Mandarin Prosodic Structure Prediction with Multi-level Contextual Information
For text-to-speech (TTS) synthesis, prosodic structure prediction (PSP) plays
an important role in producing natural and intelligible speech. Although
inter-utterance linguistic information can influence the speech interpretation
of the target utterance, previous works on PSP mainly focus on utilizing
intrautterance linguistic information of the current utterance only. This work
proposes to use inter-utterance linguistic information to improve the
performance of PSP. Multi-level contextual information, which includes both
inter-utterance and intrautterance linguistic information, is extracted by a
hierarchical encoder from character level, utterance level and discourse level
of the input text. Then a multi-task learning (MTL) decoder predicts prosodic
boundaries from multi-level contextual information. Objective evaluation
results on two datasets show that our method achieves better F1 scores in
predicting prosodic word (PW), prosodic phrase (PPH) and intonational phrase
(IPH). It demonstrates the effectiveness of using multi-level contextual
information for PSP. Subjective preference tests also indicate the naturalness
of synthesized speeches are improved.Comment: Accepted by Interspeech202
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Acoustic-Prosodic Entrainment in Human-Human and Human-Computer Dialogue
Entrainment (sometimes called adaptation or alignment) is the tendency of human speakers to adapt to or imitate characteristics of their interlocutors' behavior. This work focuses on entrainment on acoustic-prosodic features. Acoustic-prosodic entrainment has been extensively studied but is not well understood. In particular, it is difficult to compare the results of different studies, since entrainment is usually measured in different ways, reflect- ing disparate conceptualizations of the phenomenon. In the first part of this thesis, we look for evidence of entrainment on a variety of acoustic-prosodic features according to various conceptualizations, and show that human speakers of both Standard American English and Mandarin Chinese entrain to each other globally and locally, in synchrony, and that this entrainment can be constant or convergent. We explore the relationship between entrainment and gender and show that entrainment on some acoustic-prosodic features is related to social behavior and dialogue coordination. In addition, we show that humans entrain in a novel domain, backchannel-inviting cues, and propose and test a novel hypothesis: that entrainment will be stronger in the case of an outlier feature value. In the second part of the thesis, we describe a method for flexibly and dynamically entraining a TTS voice to multiple acoustic-prosodic features of a user's input utterances, and show in an exploratory study that users prefer an entraining avatar to one that does not entrain, are more likely to ask its advice, and choose more positive adjectives to describe its voice.
This work introduces a coherent view of entrainment in both familiar and novel domains. Our results add to the body of knowledge of entrainment in human-human conversations and propose new directions for making use of that knowledge to enhance human-computer interactions
Model-based Parametric Prosody Synthesis with Deep Neural Network
Conventional statistical parametric speech synthesis (SPSS) captures only frame-wise acoustic observations and computes probability densities at HMM state level to obtain statistical acoustic models combined with decision trees, which is therefore a purely statistical data-driven approach without explicit integration of any articulatory mechanisms found in speech production research. The present study explores an alternative paradigm, namely, model-based parametric prosody synthesis (MPPS), which integrates dynamic mechanisms of human speech production as a core component of F0 generation. In this paradigm, contextual variations in prosody are processed in two separate yet integrated stages: linguistic to motor, and motor to acoustic. Here the motor model is target approximation (TA), which generates syllable-sized F0 contours with only three motor parameters that are associated to linguistic functions. In this study, we simulate this two-stage process by linking the TA model to a deep neural network (DNN), which learns the “linguistic-motor” mapping given the “motor-acoustic” mapping provided by TA-based syllable-wise F0 production. The proposed prosody modeling system outperforms the HMM-based baseline system in both objective and subjective evaluations
Current trends in multilingual speech processing
In this paper, we describe recent work at Idiap Research Institute in the domain of multilingual speech processing and provide some insights into emerging challenges for the research community. Multilingual speech processing has been a topic of ongoing interest to the research community for many years and the field is now receiving renewed interest owing to two strong driving forces. Firstly, technical advances in speech recognition and synthesis are posing new challenges and opportunities to researchers. For example, discriminative features are seeing wide application by the speech recognition community, but additional issues arise when using such features in a multilingual setting. Another example is the apparent convergence of speech recognition and speech synthesis technologies in the form of statistical parametric methodologies. This convergence enables the investigation of new approaches to unified modelling for automatic speech recognition and text-to-speech synthesis (TTS) as well as cross-lingual speaker adaptation for TTS. The second driving force is the impetus being provided by both government and industry for technologies to help break down domestic and international language barriers, these also being barriers to the expansion of policy and commerce. Speech-to-speech and speech-to-text translation are thus emerging as key technologies at the heart of which lies multilingual speech processin
The SP2 SCOPES Project on Speech Prosody
This is an overview of a Joint Research Project within the Scientific co-operation between Eastern Europe and Switzerland (SCOPES) Program of the Swiss National Science Foundation (SNFS) and Swiss Agency for Development and Cooperation (SDC). Within the SP2 SCOPES Project on Speech Prosody, in the course of the following two years, the four partners aim to collaborate on the subject of speech prosody and advance the extraction, processing, modeling and transfer of prosody for a large portfolio of European languages: French, German, Italian, English, Hungarian, Serbian, Croatian, Bosnian, Montenegrin, and Macedonian. Through the intertwined four research plans, synergies are foreseen to emerge that will build a foundation for submitting strong joint proposals for EU funding
Glottal Source and Prosodic Prominence Modelling in HMM-based Speech Synthesis for the Blizzard Challenge 2009
This paper describes the CSTR entry for the Blizzard Challenge 2009. The work focused on modifying two parts of the Nitech 2005 HTS speech synthesis system to improve naturalness and contextual appropriateness. The first part incorporated an implementation of the Linjencrants-Fant (LF) glottal source model. The second part focused on improving synthesis of prosodic prominence including emphasis through context dependent phonemes. Emphasis was assigned to the synthesised test sentences based on a handful of theory based rules. The two parts (LF-model and prosodic prominence) were not combined and hence evaluated separately. The results on naturalness for the LF-model showed that it is not yet perceived as natural as the Benchmark HTS system for neutral speech. The results for the prosodic prominence modelling showed that it was perceived as contextually appropriate as the Benchmark HTS system, despite a low naturalness score. The Blizzard challenge evaluation has provided valuable information on the status of our work and continued work will begin with analysing why our modifications resulted in reduced naturalness compared to the Benchmark HTS system
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