Primjena automatskog međujezičnog akustičnog modeliranja na HMM sintezu govora za oskudne jezične baze

Nowadays Human Computer Interaction (HCI) can also be achieved with voice user interfaces (VUIs). To enable devices to communicate with humans by speech in the user\u27s own language, low-cost language portability is often discussed and analysed. One of the most time-consuming parts for the language-adaptation process of VUI-capable applications is the target-language speech-data acquisition. Such data is further used in the development of VUIs subsystems, especially of speech-recognition and speech-production systems.The tempting idea to bypass a long-term process of data acquisition is considering the design and development of an automatic algorithms, which can extract the similar target-language acoustic from different language speech databases.This paper focus on the cross-lingual phoneme mapping between an under-resourced and a well-resourced language. It proposes a novel automatic phoneme-mapping technique that is adopted from the speaker-verification field. Such a phoneme mapping is further used in the development of the HMM-based speech-synthesis system for the under-resourced language. The synthesised utterances are evaluated with a subjective evaluation and compared by the expert knowledge cross-language method against to the baseline speech synthesis based just from the under-resourced data. The results reveals, that combining data from well-resourced and under-resourced language with the use of the proposed phoneme-mapping technique, can improve the quality of under-resourced language speech synthesis.U današnje vrijeme interakcija čovjeka i računala (HCI) može se ostvariti i putem govornih sučelja (VUIs). Da bi se omogućila komunikacija uređaja i korisnika putem govora na vlastitom korisnikovom jeziku, često se raspravlja i analizira o jeftinom rješenju prijevoda govora na različite jezike. Jedan od vremenski najzahtjevnijih dijelova procesa prilagodbe jezika za aplikacije koje podržavaju VUI je prikupljanje govornih podataka za ciljani jezik. Ovakvi podaci dalje se koriste za razvoj VUI podsustava, posebice za prepoznavanje i produkciju govora. Primamljiva ideja za izbjegavanje dugotrajnog postupka prikupljanja podataka jeste razmatranje sinteze i razvoja automatskih algoritama koji su sposobni izvesti slična akustična svojstva za ciljani jezik iz postojećih baza različitih jezika.Ovaj rad fokusiran je na povezivanje međujezičnih fonema između oskudnih i bogatih jezičnih baza. Predložena je nova tehnika automatskog povezivanja fonema, usvojena i prilagođena iz područja govorne autentikacije. Ovakvo povezivanje fonema kasnije se koristi za razvoj sustava za sintezu govora zasnovanom na HMM-u za manje poznate jezike. Načinjene govorne izjave ocijenjene su subjektivnim pristupom kroz usporedbu međujezičnih metoda visoke razine poznavanja jezika u odnosu na sintezu govora načinjenu iz oskudne jezične baze. Rezultati otkrivaju da kombinacija oskudne i bogate baze jezika uz primjenu predložene tehnike povezivanja fonema može unaprijediti kvalitetu sinteze govora iz oskudne jezične baze

Transfer learning of language-independent end-to-end ASR with language model fusion

This work explores better adaptation methods to low-resource languages using an external language model (LM) under the framework of transfer learning. We first build a language-independent ASR system in a unified sequence-to-sequence (S2S) architecture with a shared vocabulary among all languages. During adaptation, we perform LM fusion transfer, where an external LM is integrated into the decoder network of the attention-based S2S model in the whole adaptation stage, to effectively incorporate linguistic context of the target language. We also investigate various seed models for transfer learning. Experimental evaluations using the IARPA BABEL data set show that LM fusion transfer improves performances on all target five languages compared with simple transfer learning when the external text data is available. Our final system drastically reduces the performance gap from the hybrid systems.Comment: Accepted at ICASSP201

Multilingual Adaptation of RNN Based ASR Systems

In this work, we focus on multilingual systems based on recurrent neural networks (RNNs), trained using the Connectionist Temporal Classification (CTC) loss function. Using a multilingual set of acoustic units poses difficulties. To address this issue, we proposed Language Feature Vectors (LFVs) to train language adaptive multilingual systems. Language adaptation, in contrast to speaker adaptation, needs to be applied not only on the feature level, but also to deeper layers of the network. In this work, we therefore extended our previous approach by introducing a novel technique which we call "modulation". Based on this method, we modulated the hidden layers of RNNs using LFVs. We evaluated this approach in both full and low resource conditions, as well as for grapheme and phone based systems. Lower error rates throughout the different conditions could be achieved by the use of the modulation.Comment: 5 pages, 1 figure, to appear in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018

Analysis of Data Augmentation Methods for Low-Resource Maltese ASR

Recent years have seen an increased interest in the computational speech processing of Maltese, but resources remain sparse. In this paper, we consider data augmentation techniques for improving speech recognition for low-resource languages, focusing on Maltese as a test case. We consider three different types of data augmentation: unsupervised training, multilingual training and the use of synthesized speech as training data. The goal is to determine which of these techniques, or combination of them, is the most effective to improve speech recognition for languages where the starting point is a small corpus of approximately 7 hours of transcribed speech. Our results show that combining the data augmentation techniques studied here lead us to an absolute WER improvement of 15% without the use of a language model.Comment: 12 page

Amharic Speech Recognition for Speech Translation

International audienceThe state-of-the-art speech translation can be seen as a cascade of Automatic Speech Recognition, Statistical Machine Translation and Text-To-Speech synthesis. In this study an attempt is made to experiment on Amharic speech recognition for Amharic-English speech translation in tourism domain. Since there is no Amharic speech corpus, we developed a read-speech corpus of 7.43hr in tourism domain. The Amharic speech corpus has been recorded after translating standard Basic Traveler Expression Corpus (BTEC) under a normal working environment. In our ASR experiments phoneme and syllable units are used for acoustic models, while morpheme and word are used for language models. Encouraging ASR results are achieved using morpheme-based language models and phoneme-based acoustic models with a recognition accuracy result of 89.1%, 80.9%, 80.6%, and 49.3% at character, morph, word and sentence level respectively. We are now working towards designing Amharic-English speech translation through cascading components under different error correction algorithms

Towards the automatic processing of Yongning Na (Sino-Tibetan): developing a 'light' acoustic model of the target language and testing 'heavyweight' models from five national languages

International audienceAutomatic speech processing technologies hold great potential to facilitate the urgent task of documenting the world's languages. The present research aims to explore the application of speech recognition tools to a little-documented language, with a view to facilitating processes of annotation, transcription and linguistic analysis. The target language is Yongning Na (a.k.a. Mosuo), an unwritten Sino-Tibetan language with less than 50,000 speakers. An acoustic model of Na was built using CMU Sphinx. In addition to this 'light' model, trained on a small data set (only 4 hours of speech from 1 speaker), 'heavyweight' models from five national languages (English, French, Chinese, Vietnamese and Khmer) were also applied to the same data. Preliminary results are reported, and perspectives for the long road ahead are outlined

Natural Language Processing for Under-resourced Languages: Developing a Welsh Natural Language Toolkit

Language technology is becoming increasingly important across a variety of application domains which have become common place in large, well-resourced languages. However, there is a danger that small, under-resourced languages are being increasingly pushed to the technological margins. Under-resourced languages face significant challenges in delivering the underlying language resources necessary to support such applications. This paper describes the development of a natural language processing toolkit for an under-resourced language, Cymraeg (Welsh). Rather than creating the Welsh Natural Language Toolkit (WNLT) from scratch, the approach involved adapting and enhancing the language processing functionality provided for other languages within an existing framework and making use of external language resources where available. This paper begins by introducing the GATE NLP framework, which was used as the development platform for the WNLT. It then describes each of the core modules of the WNLT in turn, detailing the extensions and adaptations required for Welsh language processing. An evaluation of the WNLT is then reported. Following this, two demonstration applications are presented. The first is a simple text mining application that analyses wedding announcements. The second describes the development of a Twitter NLP application, which extends the core WNLT pipeline. As a relatively small-scale project, the WNLT makes use of existing external language resources where possible, rather than creating new resources. This approach of adaptation and reuse can provide a practical and achievable route to developing language resources for under-resourced languages

Automatic Speech Recognition for Low-Resource and Morphologically Complex Languages

The application of deep neural networks to the task of acoustic modeling for automatic speech recognition (ASR) has resulted in dramatic decreases of word error rates, allowing for the use of this technology in smart phones and personal home assistants in high-resource languages. Developing ASR models of this caliber, however, requires hundreds or thousands of hours of transcribed speech recordings, which presents challenges for most of the world’s languages. In this work, we investigate the applicability of three distinct architectures that have previously been used for ASR in languages with limited training resources. We tested these architectures using publicly available ASR datasets for several typologically and orthographically diverse languages, whose data was produced under a variety of conditions using different speech collection strategies, practices, and equipment. Additionally, we performed data augmentation on this audio, such that the amount of data could increase nearly tenfold, synthetically creating higher resource training. The architectures and their individual components were modified, and parameters explored such that we might find a best-fit combination of features and modeling schemas to fit a specific language morphology. Our results point to the importance of considering language-specific and corpus-specific factors and experimenting with multiple approaches when developing ASR systems for resource-constrained languages