Ethnologue: Languages of the World

Handbook of the International Phonetic Association: A Guide to the Use of the International Phonetic Alphabet

We present the Montreal Forced Aligner (MFA), a new opensource system for speech-text alignment. MFA is an update to the Prosodylab-Aligner, and maintains its key functionality of trainability on new data, as well as incorporating improved architecture (triphone acoustic models and speaker adaptation), and other features. MFA uses Kaldi instead of HTK, allowing MFA to be distributed as a stand-alone package, and to exploit parallel processing for computationally-intensive training and scaling to larger datasets. We evaluate MFA’s performance on aligning word and phone boundaries in English conversational and laboratory speech, relative to human-annotated boundaries, focusing on the effects of aligner architecture and training on the data to be aligned. MFA performs well relative to two existing open-source aligners with simpler architecture (Prosodylab-Aligner and FAVE), and both its improved architecture and training on data to be aligned generally result in more accurate boundaries.

Montreal Forced Aligner: Trainable Text-Speech Alignment Using Kaldi.

Vanishing Voices: The Extinction of the World's Languages.

Automatic speech recognition for under-resourced languages: A survey

This paper presents first steps toward a large vocabulary continuous speech recognition system (LVCSR) for conversational Mandarin-English code-switching (CS) speech. We applied state-of-the-art techniques such as speaker adaptive and discriminative training to build the first baseline system on the SEAME corpus [1] (South East Asia Mandarin-English). For acoustic modeling, we applied different phone merging approaches based on the International Phonetic Alphabet (IPA) and Bhattacharyya distance in combination with discriminative training to improve accuracy. On language model level, we investigated statistical machine translation (SMT) - based text generation approaches for building code-switching language models. Furthermore, we integrated the provided information from a language identification system (LID) into the decoding process by using a multi-stream approach. Our best 2-pass system achieves a Mixed Error Rate (MER) of 36.6% on the SEAME development set.

/pdf/a-first-speech-recognition-system-for-mandarin-english-code-55p2dmntjy.pdf

A first speech recognition system for Mandarin-English code-switch conversational speech

This paper describes the advances in the multilingual text and speech database GlobalPhone, a multilingual database of high-quality read speech with corresponding transcriptions and pronunciation dictionaries in 20 languages. GlobalPhone was designed to be uniform across languages with respect to the amount of data, speech quality, the collection scenario, the transcription and phone set conventions. With more than 400 hours of transcribed audio data from more than 2000 native speakers GlobalPhone supplies an excellent basis for research in the areas of multilingual speech recognition, rapid deployment of speech processing systems to yet unsupported languages, language identification tasks, speaker recognition in multiple languages, multilingual speech synthesis, as well as monolingual speech recognition in a large variety of languages.

/pdf/globalphone-a-multilingual-text-speech-database-in-20-5d2hsd3pxc.pdf

GlobalPhone: A multilingual text & speech database in 20 languages

Code-switching is a very common phenomenon in multilingual communities. In this paper, we investigate language modeling for conversational Mandarin-English code-switching (CS) speech recognition. First, we investigate the prediction of code switches based on textual features with focus on Part-of-Speech (POS) tags and trigger words. Second, we propose a structure of recurrent neural networks to predict code-switches. We extend the networks by adding POS information to the input layer and by factorizing the output layer into languages. The resulting models are applied to our task of code-switching language modeling. The final performance shows 10.8% relative improvement in perplexity on the SEAME development set which transforms into a 2% relative improvement in terms of Mixed Error Rate and a relative improvement of 16.9% in perplexity on the evaluation set which leads to a 2.7% relative improvement of MER.

Recurrent neural network language modeling for code switching conversational speech

This paper presents our latest efforts toward LVCSR systems for five Eastern European languages such as Bulgarian, Croatian, Czech, Polish, and Russian using our Rapid Language Adaptation Toolkit (RLAT) [1]. We investigated the possibility of crawling large quantities of text material from the Internet, which is very cheap but also requires text post-processing steps due to the varying text quality. The goal of this study is to determine the best strategy for language model optimization on the given domain in a short time period with minimal human effort. Our results show that we can build an initial ASR system for these five languages in only twenty days using RLAT. On the multilingual GlobalPhone speech corpus [2], we achieved a word error rate (WER) of 16.9% for Bulgarian, 32.8% for Croatian, 23.5% for Czech, 20.4% for Polish, and 36.2% for Russian.

/pdf/rapid-bootstrapping-of-five-eastern-european-languages-using-2jdopznky3.pdf

Rapid bootstrapping of five eastern european languages using the rapid language adaptation toolkit.

In this paper, we analyze whether dictionaries from the World Wide Web which contain phonetic notations, may support the rapid creation of pronunciation dictionaries within the speech recognition and speech synthesis system building process. As a representative dictionary, we selected Wiktionary [1] since it is at hand in multiple languages and, in addition to the definitions of the words, many phonetic notations in terms of the International Phonetic Alphabet (IPA) are available. Given word lists in four languages English, French, German, and Spanish, we calculated the percentage of words with phonetic notations in Wiktionary. Furthermore, two quality checks were performed: First, we compared pronunciations from Wiktionary to pronunciations from dictionaries based on the GlobalPhone project, which had been created in a rule-based fashion and were manually cross-checked [2]. Second, we analyzed the impact of Wiktionary pronunciations on automatic speech recognition (ASR) systems. French Wiktionary achieved the best pronunciation coverage, containing 92.58% phonetic notations for the French GlobalPhone word list as well as 76.12% and 30.16% for country and international city names. In our ASR systems evaluation, the Spanish system gained the most improvement from Wiktionary pronunciations with 7.22% relative word error rate reduction.

/pdf/wiktionary-as-a-source-for-automatic-pronunciation-1rrxawf0us.pdf

Tim Schlippe

Papers

A first speech recognition system for Mandarin-English code-switch conversational speech

GlobalPhone: A multilingual text & speech database in 20 languages

Recurrent neural network language modeling for code switching conversational speech

Rapid bootstrapping of five eastern european languages using the rapid language adaptation toolkit.

Wiktionary as a Source for Automatic Pronunciation Extraction