Word error rate

About: Word error rate is a research topic. Over the lifetime, 11939 publications have been published within this topic receiving 298031 citations.

Hands-free voice communication in an automobile with a microphone array

Abstract: The authors present the result of their research on developing a hands-free voice communication system with a microphone array for use in an automobile environment. The goal of this research is to develop a speech acquisition and enhancement system so that a speech recognizer can reliably be used inside a noise automobile environment, for digital cellular phone application. Speech data have been collected using a microphone array and a digital audio tape (DAT) recorder inside a real car for several idling and driving conditions, and processed using delay-and-sum and adaptive beamforming algorithms. Performance criteria including signal-to-noise ratio and speech recognition error rate have been evaluated for the processed data. Detailed performance results presented show that the microphone array is superior to a single microphone. >

Utterance verification of keyword strings using word-based minimum verification error (WB-MVE) training

Abstract: An utterance verification method based on minimum verification error training is presented. In a two-stage process, the recognition hypothesis produced by an HMM-based speech recognizer is verified using a set of verification-specific models that are independent of the models used in the recognition process. The verification models are trained using a discriminative training procedure that seeks to minimize the verification error by simultaneously maximizing the rejection of non-keywords and misrecognized keywords while minimizing the rejection of correctly recognized keywords. This method is evaluated on a connected digit recognition task with a null grammar. The baseline string error rate for this task was 4.85%. At 5% rejection of valid strings, the string error rate decreased to 2.70% using the proposed verification method. The corresponding performance on non-keyword speech was a rejection rate of over 99.0%.

Distributed consensus-based demodulation: algorithms and error analysis

Abstract: This paper deals with distributed demodulation of space-time transmissions of a common message from a multi-antenna access point (AP) to a wireless sensor network. Based on local message exchanges with single-hop neighboring sensors, two algorithms are developed for distributed demodulation. In the first algorithm, sensors consent on the estimated symbols. By relaxing the finite-alphabet constraints on the symbols, the demodulation task is formulated as a distributed convex optimization problem that is solved iteratively using the method of multipliers. Distributed versions of the centralized zero-forcing (ZF) and minimum mean-square error (MMSE) demodulators follow as special cases. In the second algorithm, sensors iteratively reach consensus on the average (cross-) covariances of locally available per-sensor data vectors with the corresponding AP-to-sensor channel matrices, which constitute sufficient statistics for maximum likelihood demodulation. Distributed versions of the sphere decoding algorithm and the ZF/MMSE demodulators are also developed. These algorithms offer distinct merits in terms of error performance and resilience to non-ideal inter-sensor links. In both cases, the per-iteration error performance is analyzed, and the approximate number of iterations needed to attain a prescribed error rate are quantified. Simulated tests verify the analytical claims. Interestingly, only a few consensus iterations (roughly as many as the number of sensors), suffice for the distributed demodulators to approach the performance of their centralized counterparts.

Why word error rate is not a good metric for speech recognizer training for the speech translation task

Abstract: Speech translation (ST) is an enabling technology for cross-lingual oral communication. A ST system consists of two major components: an automatic speech recognizer (ASR) and a machine translator (MT). Nowadays, most ASR systems are trained and tuned by minimizing word error rate (WER). However, WER counts word errors at the surface level. It does not consider the contextual and syntactic roles of a word, which are often critical for MT. In the end-to-end ST scenarios, whether WER is a good metric for the ASR component of the full ST system is an open issue and lacks systematic studies. In this paper, we report our recent investigation on this issue, focusing on the interactions of ASR and MT in a ST system. We show that BLEU-oriented global optimization of ASR system parameters improves the translation quality by an absolute 1.5% BLEU score, while sacrificing WER over the conventional, WER-optimized ASR system. We also conducted an in-depth study on the impact of ASR errors on the final ST output. Our findings suggest that the speech recognizer component of the full ST system should be optimized by translation metrics instead of the traditional WER.