Multiband Modulation Energy Tracking for Noisy Speech Detection
The ability to accurately locate the boundaries of speech activity is an important attribute of any modern speech recognition, processing, or transmission system. The effort in this paper is the development of efficient, sophisticated features for speech detection in noisy environments, using ideas and techniques from recent advances in speech modeling and analysis, like presence of modulations in speech formants, energy separation and multiband filtering. First we present a method, conceptually based on a classic speech-silence discrimination procedure, that uses some newly developed, short-time signal analysis tools and provide for it a detection theoretic motivation. The new energy and spectral content representations are derived through filtering the signal in various frequency bands, estimating the Teager-Kaiser energy for each and demodulating the most active one in order to derive the signal's dominant AM-FM components. This modulation approach demonstrated an improved robustness in noise over the classic algorithm, reaching an average error reduction of 33.5% under 5-30-dB noise. Second, by incorporating alternative modulation energy features in voice activity detection, improvement in overall misclassification error of a high hit rate detector reached 7.5% and 9.5% on different benchmarks