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.

Smart dynamic selection of error correction methods for DECT based data services

Abstract: A dynamic error correction system for a bi-directional digital data transmission system. The transmission system of the present invention includes a transmitter adapted to encode information into a signal. A receiver receives the signal and decodes the information encoded thereon. The signal is transmitted from the transmitter to the receiver via a communications channel. A signal quality/error rate detector is coupled to the receiver and is adapted to detect a signal quality and/or an error rate in the information transmitted from the transmitter. The receiver is adapted to implement at least a first and second error correction process, depending upon the detected signal quality/error rate. The first error correction process is more robust and more capable than the second error correction process. The receiver coordinates the implemented error correction process with the transmitter via a feedback channel. The receiver dynamically selects the first or second error correction process for implementation in response to the detected signal quality/error rate and coordinates the selection with the transmitter such that error correction employed by the receiver and transmitter is tailored to the condition of the communications channel.

Is word error rate a good indicator for spoken language understanding accuracy

Abstract: It is a conventional wisdom in the speech community that better speech recognition accuracy is a good indicator for better spoken language understanding accuracy, given a fixed understanding component. The findings in this work reveal that this is not always the case. More important than word error rate reduction, the language model for recognition should be trained to match the optimization objective for understanding. In this work, we applied a spoken language understanding model as the language model in speech recognition. The model was obtained with an example-based learning algorithm that optimized the understanding accuracy. Although the speech recognition word error rate is 46% higher than the trigram model, the overall slot understanding error can be reduced by as much as 17%.

Near Minimax Line Spectral Estimation

Abstract: This paper establishes a nearly optimal algorithm for denoising a mixture of sinusoids from noisy equispaced samples. We derive our algorithm by viewing line spectral estimation as a sparse recovery problem with a continuous, infinite dictionary. We show how to compute the estimator via semidefinite programming and provide guarantees on its mean-squared error rate. We derive a complementary minimax lower bound on this estimation rate, demonstrating that our approach nearly achieves the best possible estimation error. Furthermore, we establish bounds on how well our estimator localizes the frequencies in the signal, showing that the localization error tends to zero as the number of samples grows. We verify our theoretical results in an array of numerical experiments, demonstrating that the semidefinite programming approach outperforms three classical spectral estimation techniques.

Personalized speech recognition on mobile devices

Abstract: We describe a large vocabulary speech recognition system that is accurate, has low latency, and yet has a small enough memory and computational footprint to run faster than real-time on a Nexus 5 Android smartphone. We employ a quantized Long Short-Term Memory (LSTM) acoustic model trained with connectionist temporal classification (CTC) to directly predict phoneme targets, and further reduce its memory footprint using an SVD-based compression scheme. Additionally, we minimize our memory footprint by using a single language model for both dictation and voice command domains, constructed using Bayesian interpolation. Finally, in order to properly handle device-specific information, such as proper names and other context-dependent information, we inject vocabulary items into the decoder graph and bias the language model on-the-fly. Our system achieves 13.5% word error rate on an open-ended dictation task, running with a median speed that is seven times faster than real-time.

Evaluation of the SPLICE algorithm on the Aurora2 database.

Abstract: This paper describes recent improvements to SPLICE, Stereobased Piecewise Linear Compensation for Environments, which produces an estimate of cepstrum of undistorted speech given the observed cepstrum of distorted speech For distributed speech recognition applications, SPLICE can be placed at the server, thus limiting the processing that would take place at the client We evaluated this algorithm on the Aurora2 task, which consists of digit sequences within the TIDigits database that have been digitally corrupted by passing them through a linear filter and/or by adding different types of realistic noises at SNRs ranging from 20dB to -5dB On set A data, for which matched training data is available, we achieved a 66% decrease in word error rate over the baseline system with clean models This preliminary result is of practical significance because in a server implementation, new noise conditions can be added as they are identified once the service is running