Transfer Learning for Speech and Language Processing

Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language, with little or no re-training data. Transfer learning is closely related to multi-task learning (cross-lingual vs. multilingual), and is traditionally studied in the name of `model adaptation'. Recent advance in deep learning shows that transfer learning becomes much easier and more effective with high-level abstract features learned by deep models, and the `transfer' can be conducted not only between data distributions and data types, but also between model structures (e.g., shallow nets and deep nets) or even model types (e.g., Bayesian models and neural models). This review paper summarizes some recent prominent research towards this direction, particularly for speech and language processing. We also report some results from our group and highlight the potential of this very interesting research field.Comment: 13 pages, APSIPA 201

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

Cross-Lingual Neural Network Speech Synthesis Based on Multiple Embeddings

The paper presents a novel architecture and method for speech synthesis in multiple languages, in voices of multiple speakers and in multiple speaking styles, even in cases when speech from a particular speaker in the target language was not present in the training data. The method is based on the application of neural network embedding to combinations of speaker and style IDs, but also to phones in particular phonetic contexts, without any prior linguistic knowledge on their phonetic properties. This enables the network not only to efficiently capture similarities and differences between speakers and speaking styles, but to establish appropriate relationships between phones belonging to different languages, and ultimately to produce synthetic speech in the voice of a certain speaker in a language that he/she has never spoken. The validity of the proposed approach has been confirmed through experiments with models trained on speech corpora of American English and Mexican Spanish. It has also been shown that the proposed approach supports the use of neural vocoders, i.e. that they are able to produce synthesized speech of good quality even in languages that they were not trained on

A prototype machine translation system between Turkmen and Turkish

In this work, we present a prototype system for translation of Turkmen texts into Turkish. Although machine translation (MT) is a very hard task, it is easier to implement a MT system between very close language pairs which have similar syntactic structure and word order. We implement a direct translation system between Turkmen and Turkish which performs a word-to-word transfer. We also use a Turkish Language Model to find the most probable Turkish sentence among all possible candidate translations generated by our system

BiSinger: Bilingual Singing Voice Synthesis

Although Singing Voice Synthesis (SVS) has made great strides with Text-to-Speech (TTS) techniques, multilingual singing voice modeling remains relatively unexplored. This paper presents BiSinger, a bilingual pop SVS system for English and Chinese Mandarin. Current systems require separate models per language and cannot accurately represent both Chinese and English, hindering code-switch SVS. To address this gap, we design a shared representation between Chinese and English singing voices, achieved by using the CMU dictionary with mapping rules. We fuse monolingual singing datasets with open-source singing voice conversion techniques to generate bilingual singing voices while also exploring the potential use of bilingual speech data. Experiments affirm that our language-independent representation and incorporation of related datasets enable a single model with enhanced performance in English and code-switch SVS while maintaining Chinese song performance. Audio samples are available at https://bisinger-svs.github.io.Comment: Accepted by ASRU202

Automated Testing of Speech-to-Speech Machine Translation in Telecom Networks

Globalisoituvassa maailmassa kyky kommunikoida kielimuurien yli käy yhä tärkeämmäksi. Kielten opiskelu on työlästä ja siksi halutaan kehittää automaattisia konekäännösjärjestelmiä. Ericsson on kehittänyt prototyypin nimeltä Real-Time Interpretation System (RTIS), joka toimii mobiiliverkossa ja kääntää matkailuun liittyviä fraaseja puhemuodossa kahden kielen välillä. Nykyisten konekäännösjärjestelmien suorituskyky on suhteellisen huono ja siksi testauksella on suuri merkitys järjestelmien suunnittelussa. Testauksen tarkoituksena on varmistaa, että järjestelmä säilyttää käännösekvivalenssin sekä puhekäännösjärjestelmän tapauksessa myös riittävän puheenlaadun. Luotettavimmin testaus voidaan suorittaa ihmisten antamiin arviointeihin perustuen, mutta tällaisen testauksen kustannukset ovat suuria ja tulokset subjektiivisia. Tässä työssä suunniteltiin ja analysoitiin automatisoitu testiympäristö Real-Time Interpretation System -käännösprototyypille. Tavoitteina oli tutkia, voidaanko testaus suorittaa automatisoidusti ja pystytäänkö todellinen, käyttäjän havaitsema käännösten laatu mittaamaan automatisoidun testauksen keinoin. Tulokset osoittavat että mobiiliverkoissa puheenlaadun testaukseen käytetyt menetelmät eivät ole optimaalisesti sovellettavissa konekäännösten testaukseen. Nykytuntemuksen mukaan ihmisten suorittama arviointi on ainoa luotettava tapa mitata käännösekvivalenssia ja puheen ymmärrettävyyttä. Konekäännösten testauksen automatisointi vaatii lisää tutkimusta, jota ennen subjektiivinen arviointi tulisi säilyttää ensisijaisena testausmenetelmänä RTIS-testauksessa.In the globalizing world, the ability to communicate over language barriers is increasingly important. Learning languages is laborious, which is why there is a strong desire to develop automatic machine translation applications. Ericsson has developed a speech-to-speech translation prototype called the Real-Time Interpretation System (RTIS). The service runs in a mobile network and translates travel phrases between two languages in speech format. The state-of-the-art machine translation systems suffer from a relatively poor performance and therefore evaluation plays a big role in machine translation development. The purpose of evaluation is to ensure the system preserves the translational equivalence, and in case of a speech-to-speech system, the speech quality. The evaluation is most reliably done by human judges. However, human-conducted evaluation is costly and subjective. In this thesis, a test environment for Ericsson Real-Time Interpretation System prototype is designed and analyzed. The goals are to investigate if the RTIS verification can be conducted automatically, and if the test environment can truthfully measure the end-to-end performance of the system. The results conclude that methods used in end-to-end speech quality verification in mobile networks can not be optimally adapted for machine translation evaluation. With current knowledge, human-conducted evaluation is the only method that can truthfully measure translational equivalence and the speech intelligibility. Automating machine translation evaluation needs further research, until which human-conducted evaluation should remain the preferred method in RTIS verification

Latest trends in hybrid machine translation and its applications

This survey on hybrid machine translation (MT) is motivated by the fact that hybridization techniques have become popular as they attempt to combine the best characteristics of highly advanced pure rule or corpus-based MT approaches. Existing research typically covers either simple or more complex architectures guided by either rule or corpus-based approaches. The goal is to combine the best properties of each type. This survey provides a detailed overview of the modification of the standard rule-based architecture to include statistical knowl- edge, the introduction of rules in corpus-based approaches, and the hybridization of approaches within this last single category. The principal aim here is to cover the leading research and progress in this field of MT and in several related applications.Peer ReviewedPostprint (published version