Sakriani Sakti

Learning to Generate Pseudo-Code from Source Code Using Statistical Machine Translation (T)

Pseudo-code written in natural language can aid the comprehension of source code in unfamiliar programming languages. However, the great majority of source code has no corresponding pseudo-code, because pseudo-code is redundant and laborious to create. If pseudo-code could be generated automatically and instantly from given source code, we could allow for on-demand production of pseudo-code without human effort. In this paper, we propose a method to automatically generate pseudo-code from source code, specifically adopting the statistical machine translation (SMT) framework. SMT, which was originally designed to translate between two natural languages, allows us to automatically learn the relationship between source code/pseudo-code pairs, making it possible to create a pseudo-code generator with less human effort. In experiments, we generated English or Japanese pseudo-code from Python statements using SMT, and find that the generated pseudo-code is largely accurate, and aids code understanding.

A postfilter to modify the modulation spectrum in HMM-based speech synthesis

In this paper, we propose a postfilter to compensate modulation spectrum in HMM-based speech synthesis. In order to alleviate over-smoothing effects which is a main cause of quality degradation in HMM-based speech synthesis, it is necessary to consider features that can capture over-smoothing. Global Variance (GV) is one well-known example of such a feature, and the effectiveness of parameter generation algorithm considering GV have been confirmed. However, the quality gap between natural speech and synthetic speech is still large. In this paper, we introduce the Modulation Spectrum (MS) of speech parameter trajectory as a new feature to effectively capture the over-smoothing effect, and we propose a postfilter based on the MS. The MS is represented as a power spectrum of the parameter trajectory. The generated speech parameter sequence is filtered to ensure that its MS has a pattern similar to natural speech. Experimental results show quality improvements when the proposed methods are applied to spectral and F0 components, compared with conventional methods considering GV.

Optimizing Segmentation Strategies for Simultaneous Speech Translation

In this paper, we propose new algorithms for learning segmentation strategies for simultaneous speech translation. In contrast to previously proposed heuristic methods, our method finds a segmentation that directly maximizes the performance of the machine translation system. We describe two methods based on greedy search and dynamic programming that search for the optimal segmentation strategy. An experimental evaluation finds that our algorithm is able to segment the input two to three times more frequently than conventional methods in terms of number of words, while maintaining the same score of automatic evaluation. 1

Developing Non-goal Dialog System Based on Examples of Drama Television

This paper presents a design and experiments of developing a non-goal dialog system by utilizing human-to-human conversation examples from drama television. The aim is to build a conversational agent that can interact with users in as natural a fashion as possible, while reducing the time requirement for database design and collection. A number of the challenging design issues we faced are described, including (1) filtering and constructing a dialog example database from the drama conversations and (2) retrieving a proper system response by finding the best dialog example based on the current user query. Subjective evaluation from a small user study is also discussed.

Postfilters to modify the modulation spectrum for statistical parametric speech synthesis

This paper presents novel approaches based on modulation spectrum (MS) for high-quality statistical parametric speech synthesis, including text-to-speech (TTS) and voice conversion (VC). Although statistical parametric speech synthesis offers various advantages over concatenative speech synthesis, the synthetic speech quality is still not as good as that of concatenative speech synthesis or the quality of natural speech. One of the biggest issues causing the quality degradation is the over-smoothing effect often observed in the generated speech parameter trajectories. Global variance (GV) is known as a feature well correlated with the over-smoothing effect, and the effectiveness of keeping the GV of the generated speech parameter trajectories similar to those of natural speech has been confirmed. However, the quality gap between natural speech and synthetic speech is still large. In this paper, we propose using the MS of the generated speech parameter trajectories as a new feature to effectively quantify the over-smoothing effect. Moreover, we propose postfilters to modify the MS utterance by utterance or segment by segment to make the MS of synthetic speech close to that of natural speech. The proposed postfilters are applicable to various synthesizers based on statistical parametric speech synthesis. We first perform an evaluation of the proposed method in the framework of hidden Markov model (HMM)-based TTS, examining its properties from different perspectives. Furthermore, effectiveness of the proposed postfilters are also evaluated in Gaussian mixture model (GMM)-based VC and classification and regression trees (CART)-based TTS (a.k.a., CLUSTERGEN). The experimental results demonstrate that 1) the proposed utterance-level postfilter achieves quality comparable to the conventional generation algorithm considering the GV, and yields significant improvements by applying to the GV-based generation algorithm in HMM-based TTS, 2) the proposed segment-level postfilter capable of achieving low-delay synthesis also yields significant improvements in synthetic speech quality, and 3) the proposed postfilters are also effective in not only HMM-based TTS but also GMM-based VC and CLUSTERGEN.

Parameter Generation Methods With Rich Context Models for High-Quality and Flexible Text-To-Speech Synthesis

In this paper, we propose parameter generation methods using rich context models as yet another hybrid method combining Hidden Markov Model (HMM)-based speech synthesis and unit selection synthesis. Traditional HMM-based speech synthesis enables flexible modeling of acoustic features based on a statistical approach. However, the speech parameters tend to be excessively smoothed. To address this problem, several hybrid methods combining HMM-based speech synthesis and unit selection synthesis have been proposed. Although they significantly improve quality of synthetic speech, they usually lose flexibility of the original HMM-based speech synthesis. In the proposed methods, we use rich context models, which are statistical models that represent individual acoustic parameter segments. In training, the rich context models are reformulated as Gaussian Mixture Models (GMMs). In synthesis, initial speech parameters are generated from probability distributions over-fitted to individual segments, and the speech parameter sequence is iteratively generated from GMMs using a parameter generation method based on the maximum likelihood criterion. Since the basic framework of the proposed methods is still the same as the traditional framework, the capability of flexibly modeling acoustic features remains. The experimental results demonstrate: (1) the use of approximation with a single Gaussian component sequence yields better synthetic speech quality than the use of EM algorithm in the proposed parameter generation method, (2) the state-based model selection yields quality improvements at the same level as the frame-based model selection, (3) the use of the initial parameters generated from the over-fitted speech probability distributions is very effective to further improve speech quality, and (4) the proposed methods for spectral and F0 components yields significant improvements in synthetic speech quality compared with the traditional HMM-based speech synthesis.

Ckylark: A More Robust PCFG-LA Parser

This paper describes Ckylark, a PCFG-LA style phrase structure parser that is more robust than other parsers in the genre. PCFG-LA parsers are known to achieve highly competitive performance, but sometimes the parsing process fails completely, and no parses can be generated. Ckylark introduces three new techniques that prevent possible causes for parsing failure: outputting intermediate results when coarse-to-fine analysis fails, smoothing lexicon probabilities, and scaling probabilities to avoid underflow. An experiment shows that this allows millions of sentences can be parsed without any failures, in contrast to other publicly available PCFG-LA parsers. Ckylark is implemented in C++, and is available opensource under the LGPL license.1

The Asian network-based speech-to-speech translation system

This paper outlines the first Asian network-based speech-to-speech translation system developed by the Asian Speech Translation Advanced Research (A-STAR) consortium. The system was designed to translate common spoken utterances of travel conversations from a certain source language into multiple target languages in order to facilitate multiparty travel conversations between people speaking different Asian languages. Each A-STAR member contributes one or more of the following spoken language technologies: automatic speech recognition, machine translation, and text-to-speech through Web servers. Currently, the system has successfully covered 9 languages— namely, 8 Asian languages (Hindi, Indonesian, Japanese, Korean, Malay, Thai, Vietnamese, Chinese) and additionally, the English language. The systems domain covers about 20,000 travel expressions, including proper nouns that are names of famous places or attractions in Asian countries. In this paper, we discuss the difficulties involved in connecting various different spoken language translation systems through Web servers. We also present speech-translation results on the first A-STAR demo experiments carried out in July 2009.

Linguistic and Acoustic Features for Automatic Identification of Autism Spectrum Disorders in Children's Narrative

Autism spectrum disorders are developmental disorders characterised as deficits in social and communication skills, and they affect both verbal and non-verbal communication. Previous works measured differences in children with and without autism spectrum disorders in terms of linguistic and acoustic features, although they do not mention automatic identification using integration of these features. In this paper, we perform an exploratory study of several language and speech features of both single utterances and full narratives. We find that there are characteristic differences between children with autism spectrum disorders and typical development with respect to word categories, prosody, and voice quality, and that these differences can be used in automatic classifiers. We also examine the differences between American and Japanese children and find significant differences with regards to pauses before new turns and linguistic cues.

Unsupervised Linear Discriminant Analysis for Supporting DPGMM Clustering in the Zero Resource Scenario

Abstract In this work we make use of unsupervised linear discriminant analysis (LDA) to support acoustic unit discovery in a zero resource scenario. The idea is to automatically find a mapping of feature vectors into a subspace that is more suitable for Dirichlet process Gaussian mixture model (DPGMM) based clustering, without the need of supervision. Supervised acoustic modeling typically makes use of feature transformations such as LDA to minimize intra-class discriminability, to maximize inter-class discriminability and to extract relevant informations from high-dimensional features spanning larger contexts. The need of class labels makes it difficult to use this technique in a zero resource setting where the classes and even their amount are unknown. To overcome this issue we use a first iteration of DPGMM clustering on standard features to generate labels for the data, that serve as basis for learning a proper transformation. A second clustering operates on the transformed features. The application of unsupervised LDA demonstrably leads to better clustering results given the unsupervised data. We show that the improved input features consistently outperform our baseline input features.