57 research outputs found
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
Extractive Text-Based Summarization of Arabic videos: Issues, Approaches and Evaluations
International audienceIn this paper, we present and evaluate a method for extractive text-based summarization of Arabic videos. The algorithm is proposed in the scope of the AMIS project that aims at helping a user to understand videos given in a foreign language (Arabic). For that, the project proposes several strategies to translate and summarize the videos. One of them consists in transcribing the Ara-bic videos, summarizing the transcriptions, and translating the summary. In this paper we describe the video corpus that was collected from YouTube and present and evaluate the transcription-summarization part of this strategy. Moreover, we present the Automatic Speech Recognition (ASR) system used to transcribe the videos, and show how we adapted this system to the Algerian dialect. Then, we describe how we automatically segment into sentences the sequence of words provided by the ASR system, and how we summarize the obtained sequence of sentences. We evaluate objectively and subjectively our approach. Results show that the ASR system performs well in terms of Word Error Rate on MSA, but needs to be adapted for dealing with Algerian dialect data. The subjective evaluation shows the same behaviour than ASR: transcriptions for videos containing dialectal data were better scored than videos containing only MSA data. However, summaries based on transcriptions are not as well rated, even when transcriptions are better rated. Last, the study shows that features, such as the lengths of transcriptions and summaries, and the subjective score of transcriptions, explain only 31% of the subjective score of summaries
Development of the Arabic Loria Automatic Speech Recognition system (ALASR) and its evaluation for Algerian dialect
International audienceThis paper addresses the development of an Automatic Speech Recognition system for Modern Standard Arabic (MSA) and its extension to Algerian dialect. Algerian dialect is very different from Arabic dialects of the Middle-East, since it is highly influenced by the French language. In this article, we start by presenting the new automatic speech recognition named ALASR (Arabic Loria Automatic Speech Recognition) system. The acoustic model of ALASR is based on a DNN approach and the language model is a classical n-gram. Several options are investigated in this paper to find the best combination of models and parameters. ALASR achieves good results for MSA in terms of WER (14.02%), but it completely collapses on an Algerian dialect data set of 70 minutes (a WER of 89%). In order to take into account the impact of the French language, on the Algerian dialect, we combine in ALASR two acoustic models, the original one (MSA) and a French one trained on ESTER corpus. This solution has been adopted because no transcribed speech data for Algerian dialect are available. This combination leads to a substantial absolute reduction of the word error of 24%. c 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 3rd International Conference on Arabic Computational Linguistics
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