Selection of spectral compressive operator for vector Taylor series-based model adaptation in noisy environments

Area of Science:

• Speech Recognition
• Acoustic Modeling
• Signal Processing

Background:

• Model adaptation algorithms are crucial for robust speech recognition systems.
• Traditional feature extraction often uses logarithmic compression (e.g., mel-frequency cepstral coefficients).
• The nonlinearity of the speech spectrum impacts the relationship between clean and noisy speech models.

Purpose of the Study:

• To investigate the effect of spectral compression methods on vector Taylor series-based model adaptation.
• To analyze the dependency of spectral nonlinearity on speech recognition performance in various noisy conditions.
• To evaluate the suitability of fractional power compression as an alternative feature extraction technique.

Main Methods:

• Utilized a vector Taylor series-based model adaptation algorithm.
• Employed fractional power compression for feature extraction, contrasting it with logarithmic compression.
• Analyzed spectral nonlinearity across different noisy environments.
• Conducted experiments to assess the impact of compressive operator choice on system performance.

Main Results:

• Demonstrated that fractional power compression is a viable alternative for feature extraction.
• Showcased that the choice of compressive operator significantly influences model adaptation performance.
• Observed improvements in speech recognition when using fractional power compression in noisy conditions.

Conclusions:

• Replacing the standard logarithmic compressive operator with fractional power compression enhances model adaptation performance.
• Spectral nonlinearity is a key factor to consider when selecting compressive operators for speech recognition.
• The findings suggest a more effective approach to feature extraction for robust speech recognition systems.