Multilingual speech recognition: the 1996 byblos callhome system
نویسندگان
چکیده
This paper describes the 1996 Byblos Callhome speech recognition system for Spanish and Egyptian Colloquial Arabic. The system uses a combination of Phoneticly Tied-Mixture Gaussian HMMs and State-Clustered Tied-Mixture Gaussian HMMs in a multiple pass decoder. We focus here on the aspects of the system which are language specific and demonstrate the adaptability of the Byblos English system to new languages. Language related issues arising from both dialectal differences as well as differences between transcribed and spoken language are discussed. This system gave the lowest error rates in both Egyptian Colloquial Arabic and Spanish in the October 1996 NIST Callhome evaluation.
منابع مشابه
The BBN Byblos 1997 large vocabulary conversational speech recognition system
This paper presents the 1997 BBN Byblos Large Vocabulary Speech Recognition (LVCSR) system. We give an outline of the algorithms and procedures used to train the system, describe the recognizer configuration and present the major technological innovations that lead to performance improvements. The major testbed we present our results for is the Switchboard Corpus, where current word error rates...
متن کاملThe 2000 BBN Byblos LVCSR system
This paper describes the 2000 BBN Byblos Large Vocabulary Continuous Speech Recognition (LVCSR) system. We briefly outline the training and decoding procedures used in the system, and explain in detail the new features we have added to the system in the past year. These new features include multiple adaptation stages, parallel path rescoring, and a new word confidence system. Word error rate re...
متن کاملRecent experiments in large vocabulary conversational speech recognition
This paper describes the improvements that resulted in the 1998 Byblos Large Vocabulary Conversational Speech Recognition (LVCSR) System. Salient among these improvements are: improved signal processing, improved Hidden Markov Model (HMM) topology, use of quinphone context, introduction of diagonal speaker adapted training (DSAT), incorporation of variance adaptation in the MLLR framework, impr...
متن کاملMultilingual Recurrent Neural Networks with Residual Learning for Low-Resource Speech Recognition
The shared-hidden-layer multilingual deep neural network (SHL-MDNN), in which the hidden layers of feed-forward deep neural network (DNN) are shared across multiple languages while the softmax layers are language dependent, has been shown to be effective on acoustic modeling of multilingual low-resource speech recognition. In this paper, we propose that the shared-hidden-layer with Long Short-T...
متن کاملLanguage-independent OCR using a continuous speech recognition system
In this paper we show how continuous speech recognition methods can be used for character recognition, resulting in a technology that is language independent and does not require presegmentation of the data at the character and word levels. In multi-font experiments on the ARPA Arabic OCR Corpus an average character error rate of 1.9% is obtained using the BBN BYBLOS Continuous Speech Recogniti...
متن کامل