Uniphore's submission to Fearless Steps Challenge Phase-2
This work addresses speech processing challenges in noisy environments for applications like audio analysis, but it is incremental as it applies existing CNN methods to a specific dataset.
The authors tackled speech activity detection and speaker identification in the Fearless Steps Challenge Phase-2 using a shared CNN architecture with mel spectrograms, achieving a detection cost function score of 5.33% on the eval set for SAD and a top-5 retrieval accuracy of 82.42% on the eval set for SID.
We propose supervised systems for speech activity detection (SAD) and speaker identification (SID) tasks in Fearless Steps Challenge Phase-2. The proposed systems for both the tasks share a common convolutional neural network (CNN) architecture. Mel spectrogram is used as features. For speech activity detection, the spectrogram is divided into smaller overlapping chunks. The network is trained to recognize the chunks. The network architecture and the training steps used for the SID task are similar to that of the SAD task, except that longer spectrogram chunks are used. We propose a two-level identification method for SID task. First, for each chunk, a set of speakers is hypothesized based on the neural network posterior probabilities. Finally, the speaker identity of the utterance is identified using the chunk-level hypotheses by applying a voting rule. On SAD task, a detection cost function score of 5.96%, and 5.33% are obtained on dev and eval sets, respectively. A top 5 retrieval accuracy of 82.07% and 82.42% are obtained on the dev and eval sets for SID task. A brief analysis is made on the results to provide insights into the miss-classified cases in both the tasks.