Advances in WP1 and WP2

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Advances in WP1 and WP2

• Paris Meeting 11 febr. 2005

www.loquendo.com
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Advances in WP1

• Paris Meeting 11 febr. 2005

www.loquendo.com
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WP1 Environment Sensor RobustnessT1.2 Noise
Independence

• Voice Activity Detection
• A Model-based approach using NN (Neural Networks)
  to discriminate two classes (noise and voice)
  will be explored
• NN input could be standard features (Cepstral
  coeff., Energy) after noise reduction, in case
  complemented by other features (pitch/voicing)
  produced by other partners (IRST)
• Training set will be multi-style, including
  several types of noise conditions and languages
• Noise Reduction
• Some noise reduction techniques will be
  experimented on the test sets selected as
  benchmarks for the project
• Spectral Subtraction (standard, Wiener and SNR
  dependent) and Spectral Attenuation
  (Ephraim-Malah SA standard and SNR dependent)
• New techniques for non-stationary noises

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WP1 Speech Databases for Noise Reduction

• Aurora 2 - Connected digits - TIdigits data down
  sampled to 8 kHz, filtered with a G712
  characteristic and noise artificially added at
  several SNRs (20dB, 15dB, 10 dB, 5dB, 0dB, -5dB).
  There are three test sets
• A same noises as in train subway, babble, car
  noise, exhibition hall
• B 4 different noises restaurant, street,
  airport, train station
• C same noises as A but filtered with a different
  microphone
• Aurora 3 - Connected digits recorded in car
  environment - Signal collected by hand free (ch1)
  and close talk (ch0) microphones. In HIWIRE we
  use Italian and Spanish recordings. There are two
  test sets
• WM ch0 and ch1 recordings used in training and
  testing lists
• HM ch0 for training and ch1 for testing
• Aurora 4 - Continuous speech 5k vocabulary - It
  is WSJ0 5K with added noise of 6 kinds Car,
  Babble, Restaurant, Street, Airport, Train
  station. It uses the standard Bi-Gram language
  modeling.

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Denoising Techniques for baseline evaluations
Spectral Subtraction (SS) operates in the
frequency domain and attempts to compute a
denoised version of the power spectrum. Wiener
spectral subtraction is defined as
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Baseline evaluations of Loquendo ASR on Aurora2
speech databases
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Baseline Performance evaluations
 

• This test was performed with the Loquendo ASR
  with the CLEAN / MULTI_CONDITION models trained
  using the Aurora2 training lists.
• The test has been done using the A/B/C testing
  lists.

Performances in terms of Word Accuracy and Error
Reduction
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Baseline evaluations of Loquendo ASR on Aurora3
speech databases
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Baseline Performance evaluations
 

• This test was performed with the Loquendo ASR and
  the models trained using the Aurora3 training
  lists.
• The test has been done using the Well Matched
  (WM) and High Mismatch (HM) testing lists.

Performances in terms of Word Accuracy and Error
Reduction
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Baseline evaluations of Loquendo ASR on Aurora4
speech databases(work in progress)
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WP1 Workplan

• selection of suitable benchmark databases (m6)
• Completion of LASR baseline experimentation of
  Spectral Subtraction (Wiener SNR dependent) (m12)
• Discriminative VAD (m16)
• Spectral Attenuation (Ephraim-Malah SA SNR
  dependent) (m18)
• Noise estimation and reduction for non-stationary
  noises (m24)

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Advances in WP2

• Paris Meeting 11 febr. 2005

www.loquendo.com
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WP2 User RobustnessT2.2 Speaker Adaptation

• Acoustic model adaptation
• Loquendo ASR is based on Hybrid HMM-NN
• Hybrid HMM-NN is an alternative to HMM modeling
  that exploits the discriminative training of MLP
  to estimate the acoustic units likelihood it is
  also very efficient for open vocabularies
• Differently from HMM, not much has been done in
  the literature for the adaptation of NN
• State-of-art NN adaptation methods
• The Linear Input Network (LIN) method has been
  proposed for speaker adaptation with promising
  results Neto 1996 Mana 2002
• The principle of LIN adaptation is to learn
  through error back-propagation the parameters of
  a linear input space transformation
• The speaker independent acoustic model (MLP) is
  kept fixed
• Innovative NN adaptation methods
• Other innovative techniques for NN adaptation
  will be proposed and experimented, including
  regularization techniques and rotations of NN
  hidden units activations

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LOQUENDO Activity in the first year

• The first activity has been the selection of
  suitable benchmark databases WSJ0 Adaptation
  component and WSJ1 Spoke-3 component
• The second activity has been the set up of
  experimental baselines for these databases, with
  standard LASR and without adaptation
• In the meantime, LIN adaptation method has been
  implemented and experimentations on the
  benchmarks are under way and will be presented at
  M12

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Speech Databases for Speaker Adaptation

• WSJ0 (standard ARPA, 1993, LDC, 1000)
• Large vocabulary (5K words) continuous speech
  database
• Test Set 8 speakers, 40 utterances, read
  speech, bigram LM
• Adaptation set the same 8 speakers, 40
  utterances each
• WSJ1 (1994,LDC, 1500)
• Similar to WSJ0, same vocabulary and LM
• SPOKE-3 standard case study of adaptation to
  non-native speakers
• 10 speakers, 40 adaptation utterances, 40 test
  utterances
• Hiwire Non-Native Speaker database
• Collected within the project
• 80 speakers, each reads 100 utterances

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WSJ0 baseline

• WSJ0 SI Test Set is made up by 8 speakers and 40
  sentences for each speaker (two microphones WV1
  Sennheiser WV2 others)
• Vocabulary 5K words, with a standard bigram LM
• The Adaptation component of WSJ0 is made up by
  the same 8 speakers of SI test, with 40
  adaptation sentences for each of them
• Only the component of adaptation and test set
  with the coherent microphone (Sennheiser -WV1)
  has been employed

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WSJ1 SPOKE-3 baseline

• Spoke-3 is the standard WSJ1 case study to
  evaluate adaptation to non-native speakers
• There are 10 non-native speakers
• For each of them there are 40 adaptation
  sentences and 40 test sentences
• Vocabulary is 5K words, with standard bigram LM
• Standard LASR for US-english has been used

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Workplan

• Selection of suitable benchmark databases (m6)
• Baseline set-up for the selected databases (m8)
• LIN adaptation method implemented and
  experimented on the benchmarks (m12)
• Regularization methods implemented and
  experimented on the benchmarks (m12)
• Innovative NN adaptation methods for acoustic
  modeling (m24)