Switching to and Combining Offline-Adapted Cluster Acoustic Models based on Unsupervised Segment Classification

40th International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2015
Switching to and Combining Offline-Adapted Cluster Acoustic Models based on Unsupervised Segment Classification
Abstract

The performance of automatic speech recognition system degrades significantly when the incoming audio differs from training data. Maximum likelihood linear regression has been widely used for unsupervised adaptation, usually in a multiple-pass recognition process. Here we present a novel adaptation framework for which the offline, supervised, high-quality adaptation is applied to clustered channel/speaker conditions that are defined with automatic and manual clustering of the training data. Upon online recognition, each speech segment is classified into one of the training clusters in an unsupervised way, and the corresponding top acoustic models are used for recognition. Recognition lattice outputs are combined. Experiments are performed on the Wall Street Journal data, and a 37.5% relative reduction of Word Error Rate is reported. The proposed approach is also compared with a general speaker adaptive training approach.

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Chaitali Gupta, Mayank Bansal, Tzu-Cheng Chuang, Ranjan Sinha, Sami Ben-romdhane

The sheer growth in data volume and Hadoop cluster size make it a significant challenge to diagnose and locate problems in a production-level cluster environment efficiently and within a short period of time. Often times, the distributed monitoring systems are not capable of detecting a problem well in advance when a large-scale Hadoop cluster starts to deteriorate i n performance or becomes unavailable. Thus, inc o m i n g workloads, scheduled between the time when cluster starts to deteriorate and the time when the problem is identified, suffer from longer execution times. As a result, both reliability and throughput of the cluster reduce significantly. In this paper, we address this problem by proposing a system called Astro, which consists of a predictive model and an extension to the Hadoop scheduler. The predictive model in Astro takes into account a rich set of cluster behavioral information that are collected by monitoring processes and model them using machine learning algorithms to predict future behavior of the cluster. The Astro predictive model detects anomalies in the cluster and also identifies a ranked set of metrics that have contributed the most towards the problem. The Astro scheduler uses the prediction outcome and the list of metrics to decide whether it needs to move and reduce workloads from the problematic cluster nodes or to prevent additional workload allocations to them, in order to improve both throughput and reliability of the cluster. The results demonstrate that the Astro scheduler improves usage of cluster compute resources significantly by 64.23% compared to traditional Hadoop. Furthermore, the runtime of the benchmark application reduced by 26.68% during the time of anomaly, thus improving the cluster throughput.

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