Stressed Speech Recognition Using Similarity Measurement on Inner Product Space

Abstract

In this paper, similarity measurement on different inner product space approach is proposed for analysis of stressed speech. The similarity is measured between neutral speech subspace and stressed speech subspace. Cosine between neutral speech and stressed speech is taken as similarity measurement parameter. It is asssumed that, speech and stress components of stressed speech are linearly related to each other. Cosine between neutral and stressed speech multiples of stressed speech contains speech information of stressed speech. Complement cosine (1-cosine) multiples of stressed speech is taken as stress component of stressed speech. Neutral speech subspace is created by all neutral speech of the training database and stressed speech subspace contain stressed (angry, sad, lombard, happy) speech. From experiment, it is observed that, stress information of stressed speech is not present in the complement cosine (1-cosine) times of stressed speech on different inner product space. The linear relationship between speech and stress component of stressed speech exists only for some specific inner product space. All the experiments are done using nonlinear (TEO-CB-Auto-Env) feature.