Noisy far-field speech recognition
The accuracy of automatic speech recognition (ASR) tasks is improved using trained models. A speech recognition model is applied in a noisy environment where speech is spoken at a distance from the microphones. The techniques may include extracting speech features, data augmentation by adding feature perturbation, and/or a multi-domain end-to-end speech recognition model. In some implementations, the described technology includes using a teacher-group knowledge distillation strategy to train a deep end-to-end speech recognition model on original speech samples and the sample speech augmentation of the original speech samples, that outputs recognized text transcriptions corresponding to speech detected in the original speech samples and the sample speech augmentation.
1. A method comprising:
training a model using audio recordings from noise scenarios in a set of training data;
decomposing a training signal from the set of training data into a message component and a noise component;
scaling the noise component by a random scale factor to obtain a scaled noise, wherein the random scale factor is a power with a base that is a constant and an exponent that includes a random variable;
adding the scaled noise to the message component to obtain a perturbed audio signal that is included in the set of training data;
training a first teacher model using a first subset of the set of training data associated with a first noise scenario of the noise scenarios;
training a second teacher model using a second subset of the set of training data associated with a second noise scenario of the noise scenarios; and
training a student model using soft labels output from the first teacher model and soft labels output from the second teacher model.
2. The method of claim 1 , wherein training the student model using soft labels output from the second teacher model comprises:
determining a label for a training signal from the set of training data as a linear interpolation of a soft label from the second teacher model and a hard label for the training signal.
3. The method of claim 1 , wherein training the student model using soft labels output from the second teacher model comprises:
randomly selecting a training signal from the set of training data;
identifying a noise scenario associated with the selected training signal; and
determining a label for the selected training signal as a linear interpolation of a hard label for the training signal and a soft label from a teacher model trained using a subset of the training data associated with the identified noise scenario.
4. The method of claim 1 , wherein the first subset of the training data associated with the first noise scenario is based on audio recordings from streets, and the second subset of the training data associated with the second noise scenario is based on audio recordings from rooms inside buildings.
5. The method of claim 1 ,
wherein the random scale factor is chosen from a range uniformly sampled in [−8 dB, −1 dB).
6. The method of claim 1 , wherein the message component is an audio signal recorded with a microphone near a desired audio source while the training signal is recorded with a microphone far from the desired audio source.
7. The method of claim 1 , wherein decomposing the training signal from the set of training data into the message component and the noise component comprises:
applying feature extraction, including a log-mel filter bank, to the training signal and to the message component; and
subtracting features of the message component from features of the training signal to obtain features of the noise component.
8. A system comprising:
a network interface,
a processor, and
a memory, wherein the memory stores instructions executable by the processor to:
train a model using audio recordings from noise scenarios in a set of training data;
decompose a training signal from the set of training data into a message component and a noise component;
scale the noise component by a random scale factor to obtain a scaled noise, wherein the random scale factor is a power with a base that is a constant and an exponent that includes a random variable;
add the scaled noise to the message component to obtain a perturbed audio signal that is included in the set of training data;
train a first teacher model using a first subset of the set of training data associated with a first noise scenario of the noise scenarios;
train a second teacher model using a second subset of the set of training data associated with a second noise scenario of the noise scenarios; and
train a student model using soft labels output from the first teacher model and soft labels output from the second teacher model.
9. The system of claim 8 , wherein the memory stores instructions executable by the processor to:
determine a label for a training signal from the set of training data as a linear interpolation of a soft label from the second teacher model and a hard label for the training signal.
10. The system of claim 8 , wherein the memory stores instructions executable by the processor to:
randomly select a training signal from the set of training data;
identify a noise scenario associated with the selected training signal; and
determine a label for the selected training signal as a linear interpolation of a hard label for the training signal and a soft label from a teacher model trained using a subset of the training data associated with the identified noise scenario.
11. The system of claim 8 , wherein the memory stores instructions executable by the processor to:
input data based on an audio signal to the student model to obtain a transcript of speech recorded in the audio signal.
12. The system of claim 8 , wherein the a random scale factor is chosen from a range uniformly sampled in [−8 dB, −1 dB).
13. The system of claim 8 , wherein the message component is an audio signal recorded with a microphone near a desired audio source while the training signal is recorded with a microphone far from the desired audio source.
14. The system of claim 8 , wherein the memory stores instructions executable by the processor to:
apply feature extraction, including a log-mel filter bank, to the training signal and to the message component; and
subtract features of the message component from features of the training signal to obtain features of the noise component.
15. A non-transitory computer-readable storage medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising:
training a model using audio recordings from noise scenarios in a set of training data;
decomposing a training signal from the set of training data into a message component and a noise component;
scaling the noise component by a random scale factor to obtain a scaled noise, wherein the random scale factor is a power with a base that is a constant and an exponent that includes a random variable;
adding the scaled noise to the message component to obtain a perturbed audio signal that is included in the set of training data;
training a first teacher model using a first subset of the set of training data associated with a first noise scenario of the noise scenarios;
training a second teacher model using a second subset of the set of training data associated with a second noise scenario of the noise scenarios; and
training a student model using soft labels output from the first teacher model and soft labels output from the second teacher model.
16. The non-transitory computer-readable storage medium of claim 15 , wherein training the student model using soft labels output from the second teacher model comprises:
determining a label for a training signal from the set of training data as a linear interpolation of a soft label from the second teacher model and a hard label for the training signal.
17. The non-transitory computer-readable storage medium of claim 15 , wherein the random scale factor is chosen from a range uniformly sampled in [−8 dB, −1 dB).