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Interview Questions

  • Here are some questions and their answers to make you ready for your next interview. Best of luck šŸ‘‹

What is the difference between Sample Rate, Bit Depth and Bit Rate?

  • Sample rate is the number of audio samples recorded per unit of time. For example, an audio with 16kHz sample rate, means that for one second, we have captured 16000 samples.
  • Bit Depth measures how precisely the samples were encoded. Here for a 16kHz sample rate audio, if the bit depth is 8 bits, it means we are using 8 bits of data to store each 16k samples per second.
  • Bit rate is the amount of bits that are recorded per unit of time. For the above example, it means we have 16k * 8 bits of data per second i.e. 128kbps

What is the difference between Frame, Frame rate, Number of Channels and Sample size?

  • Frame: one sample of the audio data per channel.
  • Frame rate: the number of times per unit time the sound data is sampled. Same as sample rate.
  • Number of channels: indicates if the audio is mono, stereo, or quadro.
  • The sample size: the size of each sample in bytes.

What is the difference between i-vector, d-vector and x-vector?

  • All of these are vector representation of the audio to capture the speaker information. Let's go through them,
    • i-vector extraction is essentially a dimensionality reduction of the GMM supervector. Refer SO Question
    • d-vector use the Long Short-Term Memory (LSTM) model to the process each individual frame (along with its context) to obtain a frame-level embedding, and average all the frame-level embeddings to obtain the segment-level embedding which can be used as the speaker embedding. Refer paper
    • x-vector take a sliding window of frames as input, and it uses Time Delay Neural Networks (TDNN) to handle the context, to get the frame-level representation. It then has a statistics pooling layer to get the mean and sd of the frame-level embeddings. And then pass the mean and sd to a linear layer to get the segment-level embedding. Refer the original Paper, OxfordWaveResearch Slides and post on r/speechtech