Recent advances in SRI'S IraqComm&amp;#x2122; Iraqi Arabic-English speech-to-speech translation system

Akbacak, Murat; Franco, Horacio; Frandsen, Michael; Hasan, Sasa; Jameel, Huda Farooq; Kathol, Andreas; Khadivi, Shahram; Lei, Xin; Mandal, Arindam; Mansour, Saab; Precoda, Kristin; Richey, Colleen; Vergyri, Dimitra; Wang, Wen; Yang, Mei; Zheng, Jing

doi:10.1109/icassp.2009.4960707

Cited by 13 publications

(5 citation statements)

References 4 publications

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“…Experiments are performed in SRI's state-of-the-art real-time Dynaspeak TM speech recognition engine in a speech-to-speech translation system [12]. Two test sets are used in the experiments: the offline and online English test sets in the July 2007 DARPA Transtac evaluation.…”

Section: Resultsmentioning

confidence: 99%

Fast likelihood computation using hierarchical Gaussian shortlists

Lei

Mandal

Zheng

2010

2010 IEEE International Conference on Acoustics, Speech and Signal Processing

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We investigate the use of hierarchical Gaussian shortlists to speed up Gaussian likelihood computation. This approach is a combination of hierarchical Gaussian selection and standard Gaussian shortlists. First, all the Gaussians are clustered hierarchically. Then, for the Gaussians in each level of the hierarchy, shortlists are trained to reduce likelihood computation at the corresponding level. This approach enables a hierarchical coarse-to-fine control of the Gaussian likelihood computation. The proposed approach is evaluated in computing the high-dimensional posteriors for feature space Minimum Phone Error (fMPE) front end and also in Viterbi search. Experimental results show that the performance of the proposed approach is superior to using only hierarchical Gaussian selection or standard Gaussian shortlists.

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Section: Resultsmentioning

confidence: 99%

Fast likelihood computation using hierarchical Gaussian shortlists

Lei

Mandal

Zheng

2010

2010 IEEE International Conference on Acoustics, Speech and Signal Processing

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“…We consider here the English-Iraqi Arabic speech-to-speech translation task presented in [15]. Only the English ASR side is considered in this paper.…”

Section: Methodsmentioning

confidence: 99%

Retrieving the syntactic structure of erroneous ASR transcriptions for open-domain Spoken Language Understanding

Béchet

Favre

Nasr

et al. 2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Retrieving the syntactic structure of erroneous ASR transcriptions can be of great interest for open-domain Spoken Language Understanding tasks in order to correct or at least reduce the impact of ASR errors on final applications. Most of the previous works on ASR and syntactic parsing have addressed this problem by using syntactic features during ASR to help reducing Word Error Rate (WER). The improvement obtained is often rather small, however the structure and the relations between words obtained through parsing can be of great interest for the SLU processes, even without a significant decrease of WER. That is why we adopt another point of view in this paper: considering that ASR transcriptions contain inevitably some errors, we show in this study that it is possible to improve the syntactic analysis of these erroneous transcriptions by performing a joint error detection / syntactic parsing process. The applicative framework used in this study is a speech-to-speech system developed through the DARPA BOLT project.

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“…The 3-gram LM had 20k unigrams, 12 million 2-grams and 20 million 3-grams. A detailed description of the ASR system is provided in [18].…”

Section: Description Of the Asr Systems Usedmentioning

confidence: 99%

Normalized amplitude modulation features for large vocabulary noise-robust speech recognition

Mitra

Franco

Graciarena

et al. 2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Background noise and channel degradations seriously constrain the performance of state-of-the-art speech recognition systems. Studies comparing human speech recognition performance with automatic speech recognition systems indicate that the human auditory system is highly robust against background noise and channel variabilities compared to automated systems. A traditional way to add robustness to a speech recognition system is to construct a robust feature set for the speech recognition model. In this work, we present an amplitude modulation feature derived from Teager's nonlinear energy operator that is power normalized and cosine transformed to produce normalized modulation cepstral coefficient (NMCC) features. The proposed NMCC features are compared with respect to state-of-the-art noise-robust features in Aurora-2 and a renoised Wall Street Journal (WSJ) corpus. The WSJ word-recognition experiments were performed on both a clean and artificially renoised WSJ corpus using SRI's DECIPHER large vocabulary speech recognition system. The experiments were performed under three train-test conditions: (a) matched, (b) mismatched, and (c) multi-conditioned. The Aurora-2 digit recognition task was performed using the standard HTK recognizer distributed with Aurora-2. Our results indicate that the proposed NMCC features demonstrated noise robustness in almost all the training-test conditions of renoised WSJ data and also improved digit recognition accuracies for Aurora-2 compared to the MFCCs and state-of-the-art noise-robust features

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Recent advances in SRI'S IraqComm™ Iraqi Arabic-English speech-to-speech translation system

Cited by 13 publications

References 4 publications

Fast likelihood computation using hierarchical Gaussian shortlists

Fast likelihood computation using hierarchical Gaussian shortlists

Retrieving the syntactic structure of erroneous ASR transcriptions for open-domain Spoken Language Understanding

Normalized amplitude modulation features for large vocabulary noise-robust speech recognition

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