Audio Word2Vec: Unsupervised Learning of Audio Segment Representations using Sequence-to-sequence Autoencoder

Yu-An Chung, Chao-Chung Wu, Chia-Hao Shen, Hung-Yi Lee, Lin-Shan Lee

National Taiwan University

InterSpeech'16

Motivation

  • Word2Vec has been shown to carry good semantic information of words in natural language processing.
  • Q: Is it possible to do similar transformation of audio segments into vectors with fixed dimensionality?
  • While Word2Vec carries semantic information of words, Audio Word2Vec carries phonetic structures of audio segments.

Background

  • Representing variable-length audio segments by vectors with fixed dimensionality has been very useful for many speech applications.
    • Speaker identification
    • Audio emotion classification
    • Spoken term detection (STD)
  • Common approach
    • Define feature vector in heuristic ways instead of learning from data.

Background

  • Recurrent Neural Network (RNN) approach
    • Take audio segments as input, the corresponding word as target (supervised).
    • The output of last few hidden layers can be taken as the representation of the input segment.
  • Pros: Able to handle variable-length input.
  • Cons: Labeled data is required.

Background

  • Autoencoder approach
    • ​Take audio segments both as input and output (unsupervised).
    • Map input segment to a vector with reduced dimensionality and reconstruct the vector back to the original input.
    • The output of hidden layer can be taken as the representation of the input segment.
  • Pros: No Labeled data is required.
  • Cons: Unable to handle variable-length input.

Background

  • Sequence-to-sequence Autoencoder (SA) approach
    • Take audio segments both as input and output (unsupervised).
    • One RNN encoder: Encode the input sequence into a fixed length representation.
    • One RNN decoder: Decode this input sequence out of that representation.
  • This approach has already been applied in NLP and video processing, but not yet in speech signals.
  • Pros: Able to handle variable-length input and no labeld data is required.

Proposed Approach

  • Recurrent Neural Network (RNN)
    • Given a sequence                                 , update hidden state      according to current input      and the previous hidden state         .
    • The hidden state      acts as an internal memory at time t that enables the network to capture dynamic temporal information.
  • Since RNN does not seem to learn long-term dependencies, LSTM units are developed to replace the neurons.
x = (x_1, x_2, ..., x_T)
x=(x1,x2,...,xT)x = (x_1, x_2, ..., x_T)
h_t
hth_t
x_t
xtx_t
h_{t-1}
ht−1h_{t-1}
h_t
hth_t

Proposed Approach

  • RNN Encoder-Decoder framework
    • Consists of one RNN encoder and one RNN decoder.
    • RNN encoder reads the input sequence                                                     and update the hidden state     .
    • RNN decoder takes hidden state      and generate the output sequence                                .
    • The length of input sequence T and output sequence T' can be different.
x = (x_1, x_2, ..., x_T)
x=(x1,x2,...,xT)x = (x_1, x_2, ..., x_T)
h_t
hth_t
h_t
hth_t
y = (y_1, y_2, ..., y_{T'})
y=(y1,y2,...,yT′)y = (y_1, y_2, ..., y_{T'})

Proposed Approach

  • Sequence-to-sequence Autoencoder (SA)
    • ​Set RNN encoder-decoder framework's target sequence to be the same as input sequence.
    • Thus the objective function becomes the  reconstruction error.

       
  • The fixed-length vector representation will be a meaningful representation since the input sequence can be reconstructed from it by the RNN Decoder.
\sum\limits_{t=1}^T \lVert x_t-y_t \rVert^2
∑t=1T∥xt−yt∥2\sum\limits_{t=1}^T \lVert x_t-y_t \rVert^2

Proposed Approach

  • Denoising Sequence-to-sequence Autoencoder (DSA)
    • ​Randomly add some noise to the input acoustic feature sequence to make the learned representation more robust.
    • DSA is expected to generate the output y closest to the original x based on the corrupted    .
    • This idea comes from the so-called denoising autoencoder.
\tilde{x}
x~\tilde{x}

Example Application

  • Query-by-example STD
    • ​Locate the occurrence regions of the input spoken query term in a large spoken archive without speech recognition.
  • Previous approach: Dynamic Time Warping (DTW)
    • ​An algorithm for measuring similarity between two temporal sequences which may vary in speed.
    • High computational cost.

Cosine similarity

Experiments

  • Dataset: LibriSpeech corpus
    • A corpus of approximately 1000 hours of 16kHz read English speech.
    • Training: 5.4 hours dev-clean (40 speakers)
    • Testing: 5.4 hours test-clean (Different 40 speakers)

Experiments

  • Experiment settings
    • ​Both training and testing are segmented by word boundaries.
    • Only the beginning 13-dim of MFCC vector is used.
    • Take k=100 for the representation's dimensionality.
    • Zero-masking technique is used to generate noisy input sequences in DSA.
    • Totally 5557 queries were made in testing set.
    • Evaluation metric: Mean Average Precision (MAP)

The minimum # of operations required to transform one string to another.

Observations

  • 2 segments for words with larger phoneme sequence edit distances have obviously smaller cosine similarity in average.
  • SA and DSA can even very clearly distinguish those word segments with only one different phoneme.
  • The cosine similarity is a little bit small since even if two audio segments are exactly the same, they can have completely different acoustic realizations.
  • DSA outperforms SA.

Observations

  • It may be possible that the last few acoustic features dominate the vector representation, and as a result those words with the same suffixes are hard to be distinguished by the learned representations.
  • However, these results show that although SA or DSA read the acoustic signals sequentially, words with the same suffix are still clearly distinguishable.

Experiments

  • 2 baselines
    • Frame-based DTW
      • Adopt vanilla version of DTW.
      • The frame-level distance is computed using Euclidean distance.

Experiments

  • 2 baselines
    • Naive Encoder (NE)
      • Divide the input sequence into m segments with length of      .
      • Average each segment vectors into one single 13-dim vector.
      • Concatenate all m average vectors to form one             -dim vector.
\frac{T}{m}
Tm\frac{T}{m}
13 \times m
13×m13 \times m

Observations

  • Besides DTW which computes the distance between audio segments directly, all other approaches map the variable-length audio segments to fixed-length vectors for similarity evaluation.
  • DTW has a poor performance.
    • Only the vanilla version was used.
    • The averaging process in NE may smooth some of the local signal variations which may cause disturbances in DTW.

Conclusions

  • The author proposed 2 unsupervised approaches which combines RNN and autoencoder to obtain fixed-length vector representations for audio segments.
  • The proposed approach outperforms the state-of-the-arts method in real world applications, such as query-by-example STD.

[InterSpeech][2016][Audio Word2Vec: Unsupervised Learning of Audio Segment Representations using Sequence-to-sequence Autoencoder]

By dreamrecord

Made with Slides.com

[InterSpeech][2016][Audio Word2Vec: Unsupervised Learning of Audio Segment Representations using Sequence-to-sequence Autoencoder]

  • 213

More from dreamrecord

  • [CIKM][2015][Improving Latent Factor Models via Personalized Feature Projection for One Class Recommendation]
    dreamrecord
    182
  • [ICDMW][2014][NLMF: NonLinear Matrix Factorization Methods for Top-N Recommender Systems]
    dreamrecord
    142