210 research outputs found
Unified model for code-switching speech recognition and language identification based on a concatenated tokenizer
Code-Switching (CS) multilingual Automatic Speech Recognition (ASR) models
can transcribe speech containing two or more alternating languages during a
conversation. This paper proposes (1) a new method for creating code-switching
ASR datasets from purely monolingual data sources, and (2) a novel Concatenated
Tokenizer that enables ASR models to generate language ID for each emitted text
token while reusing existing monolingual tokenizers. The efficacy of these
approaches for building CS ASR models is demonstrated for two language pairs,
English-Hindi and English-Spanish, where we achieve new state-of-the-art
results on the Miami Bangor CS evaluation corpus. In addition to competitive
ASR performance, the proposed Concatenated Tokenizer models are highly
effective for spoken language identification, achieving 98%+ accuracy on the
out-of-distribution FLEURS dataset
Code-Switched Urdu ASR for Noisy Telephonic Environment using Data Centric Approach with Hybrid HMM and CNN-TDNN
Call Centers have huge amount of audio data which can be used for achieving
valuable business insights and transcription of phone calls is manually tedious
task. An effective Automated Speech Recognition system can accurately
transcribe these calls for easy search through call history for specific
context and content allowing automatic call monitoring, improving QoS through
keyword search and sentiment analysis. ASR for Call Center requires more
robustness as telephonic environment are generally noisy. Moreover, there are
many low-resourced languages that are on verge of extinction which can be
preserved with help of Automatic Speech Recognition Technology. Urdu is the
most widely spoken language in the world, with 231,295,440 worldwide
still remains a resource constrained language in ASR. Regional call-center
conversations operate in local language, with a mix of English numbers and
technical terms generally causing a "code-switching" problem. Hence, this paper
describes an implementation framework of a resource efficient Automatic Speech
Recognition/ Speech to Text System in a noisy call-center environment using
Chain Hybrid HMM and CNN-TDNN for Code-Switched Urdu Language. Using Hybrid
HMM-DNN approach allowed us to utilize the advantages of Neural Network with
less labelled data. Adding CNN with TDNN has shown to work better in noisy
environment due to CNN's additional frequency dimension which captures extra
information from noisy speech, thus improving accuracy. We collected data from
various open sources and labelled some of the unlabelled data after analysing
its general context and content from Urdu language as well as from commonly
used words from other languages, primarily English and were able to achieve WER
of 5.2% with noisy as well as clean environment in isolated words or numbers as
well as in continuous spontaneous speech.Comment: 32 pages, 19 figures, 2 tables, preprin
ArzEn-ST: A Three-way Speech Translation Corpus for Code-Switched Egyptian Arabic - English
We present our work on collecting ArzEn-ST, a code-switched Egyptian Arabic -
English Speech Translation Corpus. This corpus is an extension of the ArzEn
speech corpus, which was collected through informal interviews with bilingual
speakers. In this work, we collect translations in both directions, monolingual
Egyptian Arabic and monolingual English, forming a three-way speech translation
corpus. We make the translation guidelines and corpus publicly available. We
also report results for baseline systems for machine translation and speech
translation tasks. We believe this is a valuable resource that can motivate and
facilitate further research studying the code-switching phenomenon from a
linguistic perspective and can be used to train and evaluate NLP systems.Comment: Accepted to the Seventh Arabic Natural Language Processing Workshop
(WANLP 2022
Language Model Bootstrapping Using Neural Machine Translation For Conversational Speech Recognition
Building conversational speech recognition systems for new languages is
constrained by the availability of utterances that capture user-device
interactions. Data collection is both expensive and limited by the speed of
manual transcription. In order to address this, we advocate the use of neural
machine translation as a data augmentation technique for bootstrapping language
models. Machine translation (MT) offers a systematic way of incorporating
collections from mature, resource-rich conversational systems that may be
available for a different language. However, ingesting raw translations from a
general purpose MT system may not be effective owing to the presence of named
entities, intra sentential code-switching and the domain mismatch between the
conversational data being translated and the parallel text used for MT
training. To circumvent this, we explore the following domain adaptation
techniques: (a) sentence embedding based data selection for MT training, (b)
model finetuning, and (c) rescoring and filtering translated hypotheses. Using
Hindi as the experimental testbed, we translate US English utterances to
supplement the transcribed collections. We observe a relative word error rate
reduction of 7.8-15.6%, depending on the bootstrapping phase. Fine grained
analysis reveals that translation particularly aids the interaction scenarios
which are underrepresented in the transcribed data.Comment: Accepted by IEEE ASRU workshop, 201
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