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There has been a long recognition that discrete features (n-gram features)and neural network based features have complementary strengths for languagemodels (LMs). Improved performance can be obtained by model interpolation,which is, however, a suboptimal two-step integration of discrete and neuralfeatures. The trans-dimensional random field (TRF) framework has the potentialadvantage of being able to flexibly integrate a richer set of features.However, either discrete or neural features are used alone in previous TRF LMs.This paper develops a mixed-feature TRF LM and demonstrates its advantage inintegrating discrete and neural features. Various LMs are trained over PTB andGoogle one-billion-word datasets, and evaluated in N-best list rescoringexperiments for speech recognition. Among all single LMs (i.e. without modelinterpolation), the mixed-feature TRF LMs perform the best, improving over bothdiscrete TRF LMs and neural TRF LMs alone, and also being significantly betterthan LSTM LMs. Compared to interpolating two separately trained models withdiscrete and neural features respectively, the performance of mixed-feature TRFLMs matches the best interpolated model, and with simplified one-step trainingprocess and reduced training time.

Integrating Discrete and Neural Features via Mixed-feature Trans-dimensional Random Field Language Models