  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.
