On the efficient representation and execution of deep acoustic models

In this paper we present a simple and computationally efficient quantization scheme that enables us to reduce the resolution of the parameters of a neural network from 32-bit floating point values to 8-bit integer values. The proposed quantization scheme leads to significant memory savings and enables the use of optimized hardware instructions for integer arithmetic, thus significantly reducing the cost of inference. Finally, we propose a "quantization aware" training process that applies the proposed scheme during network training and find that it allows us to recover most of the loss in accuracy introduced by quantization. We validate the proposed techniques by applying them to a long short-term memory-based acoustic model on an open-ended large vocabulary speech recognition task.Comment: Accepted conference paper: "The Annual Conference of the International Speech Communication Association (Interspeech), 2016

LiCo-Net: Linearized Convolution Network for Hardware-efficient Keyword Spotting

This paper proposes a hardware-efficient architecture, Linearized Convolution Network (LiCo-Net) for keyword spotting. It is optimized specifically for low-power processor units like microcontrollers. ML operators exhibit heterogeneous efficiency profiles on power-efficient hardware. Given the exact theoretical computation cost, int8 operators are more computation-effective than float operators, and linear layers are often more efficient than other layers. The proposed LiCo-Net is a dual-phase system that uses the efficient int8 linear operators at the inference phase and applies streaming convolutions at the training phase to maintain a high model capacity. The experimental results show that LiCo-Net outperforms single-value decomposition filter (SVDF) on hardware efficiency with on-par detection performance. Compared to SVDF, LiCo-Net reduces cycles by 40% on HiFi4 DSP

Streaming End-to-end Speech Recognition For Mobile Devices

End-to-end (E2E) models, which directly predict output character sequences given input speech, are good candidates for on-device speech recognition. E2E models, however, present numerous challenges: In order to be truly useful, such models must decode speech utterances in a streaming fashion, in real time; they must be robust to the long tail of use cases; they must be able to leverage user-specific context (e.g., contact lists); and above all, they must be extremely accurate. In this work, we describe our efforts at building an E2E speech recognizer using a recurrent neural network transducer. In experimental evaluations, we find that the proposed approach can outperform a conventional CTC-based model in terms of both latency and accuracy in a number of evaluation categories

FIABILIDAD Y VALIDEZ EN LA EVALUACIÓN DOCENTE UNIVERSITARIA (RELIABILITY AND VALIDITY ASSESSMENT IN THE UNIVERSITY TEACHING)

Resumen:El presente artículo expone una revisión bibliográfica sobre la fiabilidad y la validez de los cuestionarios de opinión estudiantil, utilizados para evaluar la competencia docente universitaria, a fin de reunir información sobre el intenso debate, su complejidad, su análisis y sobre su permanente actualidad; porque la docencia se transforma continuamente, los conocimientos y las habilidades de hoy se desactualizan mañana. Por ello, es necesario evaluar la actividad permanentemente, pero con instrumentos que cumplan cabalmente estos dos componentes psicométricos. Sin embargo, es importante recalcar que muchas de estas evaluaciones no han sido estudiadas en profundidad y en muchos casos carecen de estudios estadísticos; de ahí nacen la gran cantidad de inconvenientes y problemas que enfrentan estas evaluaciones. Además, los cuestionarios de opinión han sido contrastados con variables individuales, olvidando que la docencia es un fenómeno multidimensional integrado por un conjunto de elementos contextuales y, de esa forma, ha de estudiarse, pues algunos de ellos pueden estar asociados a factores ajenos a la docencia universitaria.Abstract:The present article, exposes an extensive bibliographical review on the reliability and validity of the questionnaires of student opinion, to evaluate the educational university competition, to bring together information of the intense debate, of It complexity and of the permanent current importance; because the teaching transforms constant, the knowledge and today skills change tomorrow, for this reason, it is necessary to evaluate the activity, with instruments that fulfill these two component psychometrics. Nevertheless, it is important to stress that many of these evaluations have not been studied in depth and lack these analyses, of there is born the great quantity of problems that face these evaluations. In addition, the questionnaires of opinion have been resisted by individual variables, forgetting that the teaching is a multidimensional phenomenon integrated to a set of elements, not isolated and of this form they have to be studied, since some of them can be associated with other factors foreign to the university teaching

FIABILIDAD Y VALIDEZ EN LA EVALUACIÓN DOCENTE UNIVERSITARIA

El presente artículo expone una revisión bibliográfica sobre la fiabilidad y la validez de los cuestionarios de opinión estudiantil, utilizados para evaluar la competencia docente universitaria, a fin de reunir información sobre el intenso debate, su complejidad, su análisis y sobre su permanente actualidad; porque la docencia se transforma continuamente, los conocimientos y las habilidades de hoy se desactualizan mañana. Por ello, es necesario evaluar la actividad permanentemente, pero con instrumentos que cumplan cabalmente estos dos componentes psicométricos. Sin embargo, es importante recalcar que muchas de estas evaluaciones no han sido estudiadas en profundidad y en muchos casos carecen de estudios estadísticos; de ahí nacen la gran cantidad de inconvenientes y problemas que enfrentan estas evaluaciones. Además, los cuestionarios de opinión han sido contrastados con variables individuales, olvidando que la docencia es un fenómeno multidimensional integrado por un conjunto de elementos contextuales y, de esa forma, ha de estudiarse, pues algunos de ellos pueden estar asociados a factores ajenos a la docencia universitaria