Training a conventional automatic speech recognition (ASR) system to support multiple languages is challenging because the sub-word unit, lexicon and word inventories are typically language specific. In contrast, sequence-to-sequence models are well suited for multilingual ASR because they encapsulate an acoustic, pronunciation and language model jointly in a single network. In this work we present a single sequence-to-sequence ASR model trained on 9 different Indian languages, which have very little overlap in their scripts. Specifically, we take a union of language-specific grapheme sets and train a grapheme-based sequence-to-sequence model jointly on data from all languages. We find that this model, which is not explicitly given any information about language identity, improves recognition performance by 21% relative compared to analogous sequence-to-sequence models trained on each language individually. By modifying the model to accept a language identifier as an additional input feature, we further improve performance by an additional 7% relative and eliminate confusion between different languages.

Multilingual Speech Recognition With A Single End-To-End Model

State-of-the art approaches to speech synthesis are unit selection based concatenative speech synthesis (USS) and hidden Markov model based Text to speech synthesis (HTS). The former is based on waveform concatenation of subword units, while the latter is based on generation of an optimal parameter sequence from subword HMMs. The quality of an HMM based synthesiser in the HTS framework, crucially depends on an accurate description of the phoneset, and accurate description of the question set for clustering of the phones. Given the number of Indian languages, building a HTS system for every language is time consuming. Exploiting the properties of Indian languages, a uniform HMM framework for building speech synthesisers is proposed. Apart from the speech and text data used, the tasks involved in building a synthesis system can be made language-independent. A language-independent common phone set is first derived. Similar articulatory descriptions also hold for sounds that are similar. The common phoneset and common question set are used to build HTS based systems for six Indian languages, namely, Hindi, Marathi, Bengali, Tamil, Telugu and Malayalam. Mean opinion score (MOS) is used to evaluate the system. An average MOS of 3.0 for naturalness and 3.4 for intelligibility is obtained for all languages.

A common attribute based unified HTS framework for speech synthesis in Indian languages.

In this paper spectral and prosodic features extracted from different levels are explored for analyzing the language specific information present in speech. In this work, spectral features extracted from frames of 20 ms (block processing), individual pitch cycles (pitch synchronous analysis) and glottal closure regions are used for discriminating the languages. Prosodic features extracted from syllable, tri-syllable and multi-word (phrase) levels are proposed in addition to spectral features for capturing the language specific information. In this study, language specific prosody is represented by intonation, rhythm and stress features at syllable and tri-syllable (words) levels, whereas temporal variations in fundamental frequency (F 0 contour), durations of syllables and temporal variations in intensities (energy contour) are used to represent the prosody at multi-word (phrase) level. For analyzing the language specific information in the proposed features, Indian language speech database (IITKGP-MLILSC) is used. Gaussian mixture models are used to capture the language specific information from the proposed features. The evaluation results indicate that language identification performance is improved with combination of features. Performance of proposed features is also analyzed on standard Oregon Graduate Institute Multi-Language Telephone-based Speech (OGI-MLTS) database.

Identification of Indian languages using multi-level spectral and prosodic features

To build a natural sounding speech synthesis system, it is essential that the text processing component produce an appropriate sequence of phonemic units corresponding to an arbitrary input text. In this paper we discuss our efforts in addressing the issues of Font-to-Akshara mapping, pronunciation rules for Aksharas, text normalization in the context of building text-to-speech systems in Indian languages.

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Text processing for text-to-speech systems in Indian languages.

This paper describes the design of a language independent parser for text-to-speech synthesis in Indian languages. Indian languages come from 5–6 different language families of the world. Most Indian languages have their own scripts. This makes parsing for text to speech systems for Indian languages a difficult task. In spite of the number of different families which leads to divergence, there is a convergence owing to borrowings across language families. Most importantly Indian languages are more or less phonetic and can be considered to consist broadly of about 35–38 consonants and 15–18 vowels. In this paper, an attempt is made to unify the languages based on this broad list of phones. A common label set is defined to represent the various phones in Indian languages. A uniform parser is designed across all the languages capitalising on the syllable structure of Indian languages. The proposed parser converts UTF-8 text to common label set, applies letter-to-sound rules and generates the corresponding phoneme sequences. The parser is tested against the custom-built parsers for multiple Indian languages. The TTS results show that the accuracy of the phoneme sequences generated by the proposed parser is more accurate than that of language specific parsers.

A Unified Parser for Developing Indian Language Text to Speech Synthesizers

Transliteration editors are essential for keying-in Indian language scripts into the computer using QWERTY keyboard. Applications of transliteration editors in the context of Universal Digital Library (UDL) include entry of meta-data and dictionaries for Indian languages. In this paper we propose a simple approach for building transliteration editors for Indian languages using Unicode and by taking advantage of its rendering engine. We demonstrate the usefulness of the Unicode based approach to build transliteration editors for Indian languages, and report its advantages needing little maintenance and few entries in the mapping table, and ease of adding new features such as adding letters, to the transliteration scheme. We demonstrate the transliteration editor for 9 Indian languages and also explain how this approach can be adapted for Arabic scripts.

A simple approach for building transliteration editors for Indian languages

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Prahallad Lavanya

Papers

A simple approach for building transliteration editors for Indian languages

Purpose and Use of Library Information Services by Telangana Journalists