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.

Voiced/Unvoiced classification of speech with applications to the U.S. government LPC-10E algorithm

Abstract: This paper describes the development and application of a new voicing algorithm used in the 2400 bit per second U.S. Government's Enhanced Linear Predictive Coder (LPC-10E). Correct voicing is crucial to perceived quality and naturalness of LPC systems and therefore to user acceptance of LPC systems. This new voicing algorithm uses a smoothed adaptive linear discriminator to classify the signal as voiced or unvoiced speech. The classifier was determined using Fisher's method of linear discriminant analysis. The voicing decision smoother is a modified median smoother that uses both the linear discriminant and speech onsets to determine its smoothing. The voicing classifier adapts to various acoustic noise levels and features a powerful new set of signal measurements: biased zero crossing rate, energy measures, reflection coefficients, and prediction gains. The LPC-10E voicing algorithm improves upon other 2400 bps LPC voicing algorithms by providing higher quality synthesized speech. Higher quality is due to halving of the error rate and graceful degradation in the presence of acoustic noise.

Lowering LDPC Error Floors by Postprocessing

Abstract: A class of combinatorial structures, called absorbing sets, strongly influences the performance of low-density parity-check (LDPC) decoders at low error rates. Past experiments have shown that a class of (8,8) absorbing sets determines the error floor performance of the (2048,1723) Reed-Solomon based LDPC code (RS-LDPC). A postprocessing approach is formulated to exploit the structure of the absorbing set by biasing the reliabilities of selected messages in a message-passing decoder. The approach converges quickly and can be efficiently implemented with minimal overhead. Hardware emulation of the decoder with postprocessing shows more than two orders of magnitude improvement in the very low bit error rate performance and error- floor-free operation below a BER of 10-12.

Discriminative Syntactic Language Modeling for Speech Recognition

Abstract: We describe a method for discriminative training of a language model that makes use of syntactic features. We follow a reranking approach, where a baseline recogniser is used to produce 1000-best output for each acoustic input, and a second "reranking" model is then used to choose an utterance from these 1000-best lists. The reranking model makes use of syntactic features together with a parameter estimation method that is based on the perception algorithm. We describe experiments on the Switchboard speech recognition task. The syntactic features provide an additional 0.3% reduction in test-set error rate beyond the model of (Roark et al., 2004a; Roark et al., 2004b) (significant at p < 0.001), which makes use of a discriminatively trained n-gram model, giving a total reduction of 1.2% over the baseline Switchboard system.

Arrangement for the optimization of the data transmission via a bi-directional radio channel

Abstract: The arrangement is for optimization of data transmission via a bi-directional radio channel in which respective types of modulation can be selected at the transmitter side and the code rate of the forward error correction (FEC) as well as the power of the transmitter Devices (CRC) are provided at the reception side for determination of the error rate. The size of the data packets, and/or the type of modulation, and/or the code rate, and/or the power of the transmitter is varied, dependent on the error rate transmitted back, such that a predetermined error rate is achieved at the reception side.

Towards an automatic sign language recognition system using subunits

Abstract: This paper is concerned with the automatic recognition of German continuous sign language. For the most user-friendliness only one single color video camera is used for image recording. The statistical approach is based on the Bayes decision rule for minimum error rate. Following speech recognition system design, which are in general based on subunits, here the idea of an automatic sign language recognition system using subunits rather than models for whole signs will be outlined. The advantage of such a system will be a future reduction of necessary training material. Furthermore, a simplified enlargement of the existing vocabulary is expected. Since it is difficult to define subunits for sign language, this approach employs totally self-organized subunits called fenone. K-means algorithm is used for the definition of such fenones. The software prototype of the system is currently evaluated in experiments.