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—We describe the design of Kaldi, a free, open-source toolkit for speech recognition research. Kaldi provides a speech recognition system based on finite-state transducers (using the freely available OpenFst), together with detailed documentation and scripts for building complete recognition systems. Kaldi is written is C++, and the core library supports(More)
Sequence-discriminative training of deep neural networks (DNNs) is investigated on a 300 hour American English conversational telephone speech task. Different sequence-discriminative criteria — maximum mutual information (MMI), minimum phone error (MPE), state-level minimum Bayes risk (sMBR), and boosted MMI — are compared. Two different heuristics are(More)
This paper describes the speech activity detection (SAD) system developed by the Patrol team for the first phase of the DARPA RATS (Robust Automatic Transcription of Speech) program, which seeks to advance state of the art detection capabilities on audio from highly degraded communication channels. We present two approaches to SAD, one based on Gaussian(More)
In this paper, we investigate alternative ways of processing MFCC-based features to use as the input to Deep Neural Networks (DNNs). Our baseline is a conventional feature pipeline that involves splicing the 13-dimensional front-end MFCCs across 9 frames, followed by applying LDA to reduce the dimension to 40 and then further decorrelation using MLLT.(More)
We describe a lattice generation method that is exact, i.e. it satisfies all the natural properties we would want from a lattice of alternative transcriptions of an utterance. This method does not introduce substantial overhead above one-best decoding. Our method is most directly applicable when using WFST decoders where the WFST is “fully(More)
The neural network based features became an inseparable part of state-of-the-art LVCSR systems. In order to perform well, the network has to be trained on a large amount of in-domain data. With the increasing emphasis on fast development of ASR system on limited resources, there is an effort to alleviate the need of in-domain data. To evaluate the(More)
We submitted two approaches as the required runs: Acoustic Keyword Spotting as the primary one (AKWS) and Dynamic Time Wrapping as the secondary one (DTW) for the Spoken Web Search task. We aimed at building a simple phone based language-dependent system. We experimented with universal context bottleneck neural network classifier with 3-state phone(More)