Joint morphological-lexical language modeling for processing morphologically rich languages with application to dialectal Arabic

Language modeling for an inflected language such as Arabic poses new challenges for speech recognition and machine translation due to its rich morphology. Rich morphology results in large increases in out-of-vocabulary (OOV) rate and poor language model parameter estimation in the absence of large quantities of data. In this study, we present a joint morphological-lexical language model (JMLLM) that takes advantage of Arabic morphology. JMLLM combines morphological segments with the underlying lexical items and additional available information sources with regards to morphological segments and lexical items in a single joint model. Joint representation and modeling of morphological and lexical items reduces the OOV rate and provides smooth probability estimates while keeping the predictive power of whole words. Speech recognition and machine translation experiments in dialectal-Arabic show improvements over word and morpheme based trigram language models. We also show that as the tightness of integration between different information sources increases, both speech recognition and machine translation performances improve

Comparison between rule-based and data-driven natural language processing algorithms for Brazilian Portuguese speech synthesis

Due to the exponential growth in the use of computers, personal digital assistants and smartphones, the development of Text-to-Speech (TTS) systems have become highly demanded during the last years. An important part of these systems is the Text Analysis block, that converts the input text into linguistic specifications that are going to be used to generate the final speech waveform. The Natural Language Processing algorithms presented in this block are crucial to the quality of the speech generated by synthesizers. These algorithms are responsible for important tasks such as Grapheme-to-Phoneme Conversion, Syllabification and Stress Determination. For Brazilian Portuguese (BP), solutions for the algorithms presented in the Text Analysis block have been focused in rule-based approaches. These algorithms perform well for BP but have many disadvantages. On the other hand, there is still no research to evaluate and analyze the performance of data-driven approaches that reach state-of-the-art results for complex languages, such as English. So, in this work, we compare different data-driven approaches and rule-based approaches for NLP algorithms presented in a TTS system. Moreover, we propose, as a novel application, the use of Sequence-to-Sequence models as solution for the Syllabification and Stress Determination problems. As a brief summary of the results obtained, we show that data-driven algorithms can achieve state-of-the-art performance for the NLP algorithms presented in the Text Analysis block of a BP TTS system.Nos últimos anos, devido ao grande crescimento no uso de computadores, assistentes pessoais e smartphones, o desenvolvimento de sistemas capazes de converter texto em fala tem sido bastante demandado. O bloco de análise de texto, onde o texto de entrada é convertido em especificações linguísticas usadas para gerar a onda sonora final é uma parte importante destes sistemas. O desempenho dos algoritmos de Processamento de Linguagem Natural (NLP) presentes neste bloco é crucial para a qualidade dos sintetizadores de voz. Conversão Grafema-Fonema, separação silábica e determinação da sílaba tônica são algumas das tarefas executadas por estes algoritmos. Para o Português Brasileiro (BP), os algoritmos baseados em regras têm sido o foco na solução destes problemas. Estes algoritmos atingem bom desempenho para o BP, contudo apresentam diversas desvantagens. Por outro lado, ainda não há pesquisa no intuito de avaliar o desempenho de algoritmos data-driven, largamente utilizados para línguas complexas, como o inglês. Desta forma, expõe-se neste trabalho uma comparação entre diferentes técnicas data-driven e baseadas em regras para algoritmos de NLP utilizados em um sintetizador de voz. Além disso, propõe o uso de Sequence-to-Sequence models para a separação silábica e a determinação da tonicidade. Em suma, o presente trabalho demonstra que o uso de algoritmos data-driven atinge o estado-da-arte na performance dos algoritmos de Processamento de Linguagem Natural de um sintetizador de voz para o Português Brasileiro

Towards Language-Universal End-to-End Speech Recognition

Building speech recognizers in multiple languages typically involves replicating a monolingual training recipe for each language, or utilizing a multi-task learning approach where models for different languages have separate output labels but share some internal parameters. In this work, we exploit recent progress in end-to-end speech recognition to create a single multilingual speech recognition system capable of recognizing any of the languages seen in training. To do so, we propose the use of a universal character set that is shared among all languages. We also create a language-specific gating mechanism within the network that can modulate the network's internal representations in a language-specific way. We evaluate our proposed approach on the Microsoft Cortana task across three languages and show that our system outperforms both the individual monolingual systems and systems built with a multi-task learning approach. We also show that this model can be used to initialize a monolingual speech recognizer, and can be used to create a bilingual model for use in code-switching scenarios.Comment: submitted to ICASSP 201

Using question-specific vocabularies to support speech data collection with SALAAM

There has been an increasing use of small-vocabulary spoken dialogue systems in low-resource settings for information dissemination and data collection. This provides an opportunity to reduce the information gap in low-resource settings in which low-literacy is a huge hindrance to the adoption of Information Communication Technologies (ICTs). Since the languages spoken in these areas are computationally low-resourced, they rely on techniques such as crosslanguage phoneme mapping to facilitate fast development of small-vocabulary speech recognisers. Despite the success of this technique, there has been a lack of guidance on how to deploy such systems across a range of languages. This study presents a systematic exploration of the suitability and limitations of using crosslanguage phoneme mapping for the development of small-vocabulary speech recognisers for computationally low-resource languages, particularly Bantu languages. Five target languages and four source languages were used in the study. Speech-based Accent Learning And Articulation Mapping (SALAAM), a cross-language phoneme mapping algorithm was used to aid the study based on its implementation in an open-source tool Lex4All. The following research questions guided our investigations: i) What impact does source language choice have on recognition accuracy, ii) What impact does gender composition of the training data set have on recognition accuracy and iii) What impact do varied alternative pronunciations per word type have on recognition accuracy. Data for the target languages was collected from 104 university student volunteers consisting of 58 female and 46 male students. The results showed that target and source language phonetic similarity as well as gender composition of the training datasets affects recognition accuracy of speech applications developed using cross-language phoneme mapping techniques. They also showed that increasing the number of alternative pronunciations per word in the vocabulary generally increases recognition accuracy although with a slower system response time. This study provides evidence that a careful selection of the source language, gender composition of the training data and the number of alternative pronunciations per word can improve the recognition accuracy of speech recognisers developed using cross-language phoneme mapping