Cross-system adaptation and combination for continuous speech recognition: the influence of phoneme set and acoustic front-end
Sebastian Stüker, Christian Fügen, Susanne Burger, Matthias Wölfel · 2006
Abstract Cross-system adaptation and system combination methods,such as ROVER and confusion network combination, areknown to lower the word error rate of speech recognitionsystems. They require the training of systems that are rea-sonably close in performance but at the same time produceoutput that differs in its errors. This provides complemen-taryinformationwhichleadstoperformanceimprovements.In this paper we demonstrate the gains we have seen withcross-systemadaptationandsystemcombinationontheEn-glish EPPS and RT0-05S lecture meeting task. We obtainedthe necessary varying systems by using different acous-tic front-ends and phoneme sets on which our models arebased. Inasetofcontrastiveexperimentsweshowtheinflu-ence that the exchange of the components has on adaptationand system combination.Index Terms: automatic speech recognition, system com-bination, cross adaptation, EPPS, RT-05S. 1. Introduction In state-of-the-art speech recognition systems it is commonpractice to use multi-pass systems with adaptation of theacoustic model in-between passes. The adaptation aims atbetter fitting the system to the speakers and/or acoustic en-vironmentsfoundinthetestdata. Itisusuallyperformedona by-speaker basis, obtained either from manual speaker la-bels or automatic clustering methods. Common adaptationmethods try to transform either the models used in a systemor the features to which the models are applied.Three adaptation methods that can be found in manystate-of-the-art systems are Maximum Likelihood LinearRegression (MLLR) [1], a model transformation, Vo-cal Tract Length Normalization (VTLN) [2] and feature-space constrained MLLR (fMLLR) [3], two feature-transformation methods. Adaptation is performed in an un-supervisedmanner,suchthattheerror-pronehypothesesob-tainedfromthepreviousdecodingpassaretakenasthenec-essary reference for adaptation. Generally, the word errorrates of the hypotheses obtained from the adapted systemsarelowerthanthoseforhypothesesonwhichtheadaptationwas performed. This sequences of adaption and decodingmake it possible to incrementally improve the performanceof the recognition system. Unfortunately, this loop of adap-tation and decoding does not always lead to significant im-provements. Often, after two or three stages of adapting asystem on its own output, no more gains can be obtained.This problem can be overcome by adapting a system