View source for Deep Speech Factorization

=Project name=
Deep Factorization for Speech Signals

=Project members=
Dong Wang, Lantian Li, Zhiyuan Tang

=Introduction=


Various informative factors mixed in speech signals, leading to great difficulty when decoding any of the factors.
Here informative factors we mean task-oriented factors, e.g., linguistic content, speaker identity and emotion factor, rather 
than factors of physical models, e.g., excitation and modulation of the traditional source-filter model.


A natural idea is that, if we can factorize speech signals into individual information factors, all the speech 
signal processing tasks will be greatly simplified. However, as expected, this factorization is highly difficult,
due to at least two reasons: firstly, the mechanism that various factors mixed together is far from clear to us,
the only thing we know right now is it is rather complex; secondly, if some typical factors are short-time identifiable 
is also far from known, e.g., speaker traits. That is to say, we essentially do not know if speech signals are short-time
factorizable, until recently we found a deep speaker feature learning approach recently[1].

The discovery that speech signals are short-time factorizable is important, and it opens a door to a new paradigm of 
speech research. This project follows this direction and attempts to lay down the foundation of this new science. 


==Speaker feature learning==

The discovery of the short-time property of speaker traits is the key step towards speech signal factorization, as 
the speaker trait is one of the two main factors: the other is linguistic content that we have known for a long time
being short-time patterns. 

The key idea of speaker feature learning is simply based on the idea of discriminating training speakers based on 
short-time frames by deep neural networks (DNN), date back to 2014 by Ehsan et al.[2]. As shown below, the output of the DNN 
involves the training speakers, and the frame-level speaker features are read from the last hidden layer. The 
basic assumption here is: if the output of the last hidden layer can be used as the input feature of the 
last hidden layer (a software regression classifier), these features should be speaker discriminative. 

[[文件:Dnn-spk.png|500px]]

However, the vanilla structure of Ehsan et al. performs rather poor compared to the i-vector counterpart. One reason is 
that the simple back-end scoring is based on average to derive the utterance-based representations (called d-vectors) , but
another reason is the vanilla DNN structure that does not consider much of the context and pattern learning. We therefore
proposed a CT-DNN model that can learn stronger speaker features. The structure is shown below[1]:

[[文件:Ctdnn-spk.png|500px]]


Recently, we found that an 'all-info' training is effective for learning features. Looking back to DNN and CT-DNN, although the features 
read from last hidden layer are discriminative, but not 'all discriminative', because some discriminant info can be also impelemented
in the last affine layer. A better strategy is let the feature generation net (feature net) learns all the things of discrimination. 
To achieve this, we discarded the parametric classifier (the last affine layer) and use the simple cosine distance to conduct the
classification. An iterative training scheme can be used to implement this idea, that is, after each epoch, averaging the speaker
features to derive speaker vectors, and then use the speaker vectors to replace the last hidden layer. The training will be then
taken as usual. The new structure is as follows[4]:


[[文件:fullinfo-spk.png|500px]]


=Speech factorization=


The short-time property is a very nice thing, which tells us it is possible to factorize speech signals. By factorization, we can achieve significant benefits:

A. Individual tasks can be largely improved, as unrelated factors have been removed.

B. Factors that are disturbs now becomes valuables things, leading to conditional training and collaborative training [5].

C. Once the factors have been separated, single factors can be manipulated, and reassemble these factors can change the signal according to the need.

D. It is a new speech coding scheme that leverage knowledge learned from large data.


Traditional factorization methods are based on probabilistic models and maximum likelihood learning. For example, in JFA, a linear Gaussian is assumed 
for speaker and channel, and then a ML estimation is applied to estimate the loading matrices of each factor, based on a long duration of speech. Almost
all these factorizations share these features: shallow, linear, Gaussian, long-term segments. 

We are more interested in factorization on frame-level, and plays not much assumption on how the factors are mixed. A cascaded factorization approach has been 
proposed[6]. The basic idea is to factorize significant factors first,and then conditioned on the factors that have been derived. The architecture is as 
follows, where we factorized speech signals into three factors: linguistic content, speaker trait, emotion. When factorizing each factor, supervised
learning is used. Note by this architecture, databases with different target labels can be used in a complementary way, which is different from 
previously joint training approach that needs full-labelled data.


[[文件:deepfact.png|500px]]


=Speech reconstruction=

To verify the factorization, we can reconstruct the speech signal from the factors. The reconstruction is simply based on a DNN,
as shown below.
Each factor passes a unique deep neural net, the output of the three DNNs are added together, and compared with the target,
which is the logarithm of the spectrum of the original signal. This means that the output of the DNNs of the three factors are 
assumed to be convolved together to produce the original speech. 

[[文件:fact-recover-dnn.png|500px]]

Note that the factors are learned from Fbanks, by which some speech information 
has been lost, however the recovery is rather successfull. 


==View the reconstruction==

[[文件:fact-recover.png|500px]]


More recovery examples can be found [[dsf-examples|here]].


==Listen to the reconstruction==

We can listen to the wave for each factor, by using the original phase. 


Original speech: [http://wangd.cslt.org/research/cdf/demo/CHEAVD_1_1_E02_001_worried.wav]

Linguistic factor: [http://wangd.cslt.org/research/cdf/demo/phone.wav]

Speaker factor: [http://wangd.cslt.org/research/cdf/demo/speaker.wav]

Emotion factor: [http://wangd.cslt.org/research/cdf/demo/emotion.wav]

Liguistic+ Speaker + Emotion: [http://wangd.cslt.org/research/cdf/demo/recovery.wav]


=Research directions=

* Adversarial factor learning
* Phone-aware multiple d-vector back-end for speaker recognition
* TTS adaptation based on speaker factors


=Reference=

[1] Lantian Li, Yixiang Chen, Ying Shi, Zhiyuan Tang, and Dong Wang, “Deep speaker feature learning for text-independent speaker verification,”, Interspeech 2017. 

[2] Ehsan Variani, Xin Lei, Erik McDermott, Ignacio Lopez Moreno, and Javier Gonzalez-Dominguez, “Deep neural networks for small footprint text-dependent speaker
verification,”, ICASSP 2014.

[3] Lantian Li, Dong Wang, Yixiang Chen, Ying Shing, Zhiyuan Tang, http://wangd.cslt.org/public/pdf/spkfact.pdf

[4] Lantian Li, Zhiyuan Tang, Dong Wang, FULL-INFO TRAINING FOR DEEP SPEAKER FEATURE LEARNING, http://wangd.cslt.org/public/pdf/mlspk.pdf

[5] Zhiyuan Thang, Lantian Li, Dong Wang, Ravi Vipperla "Collaborative Joint Training with Multi-task Recurrent Model for Speech and Speaker Recognition", IEEE Trans. on Audio, Speech and Language Processing, vol. 25, no.3, March 2017.

[6] Dong Wang,Lantian Li,Ying Shi,Yixiang Chen,Zhiyuan Tang., "Deep Factorization for Speech Signal", https://arxiv.org/abs/1706.01777