15Can you guess the age from this voice?
Deep speaker attribute estimation with speaker clustering

Abstract
Estimating speaker-attributes such as age and gender is an important task with a wide range of applications.
While the recent proposed deep neural network models have been achieving high performance, the estimatedresults tend to be less reliable because of the overfitting problem. In order to solve this problem, we propose ageneral framework for correcting the unreliable results of the arbitrary speaker-attribute estimation models. The
proposed algorithm first applies speaker clustering to the target utterances to detect similar speakers of target
utterances. Then, the speaker-attribute class of each cluster is determined by voting on the utterances assigned
to the cluster. Finally, we can correct the result of unreliable utterances by replacing their result with the clusters’
speaker-attribute class. Our approach is evaluated on age-gender classification and gender regression tasks,
yielding significant improvements in classification accuracy and mean absolute error.

Deep speaker attribute estimation

Error correction based on speaker clustering

Problem definition

Correct estimation results of deep learning modelby majority voting of results of similar speakers

Estimate speakers’ age and gender from their voiceswith deep learning modelSet of target utterances

Applications

Deep learning
model

Call center response decision, marketing support,
realization of a voice dialogue system that changes
behavior according to user attributes, etc.
1-1. Apply clustering
based on speakersimilarity
Age: ??
male / female

Deep speakerattribute estimation
model

Difficulties of task
• There is large deviation inamount of training data foreach age-group (Fig.1)• Models tend to beoverfitting to specificspeaker / age (Fig. 2)

Fig.1 Age histogram ofNIST-SRE08● Male_Child● Male_Adult● Male_Elderly
× Female_Child× Female_Adult× Female_Elderly

1-2. Estimate age classof each utterance bydeep learning model

Adult → Elderly(Replaced with cluster’s class)
2. Decide age class ofElderlyeach cluster by voting
C1 = ElderlyChildElderly
3. Replace result of eachSpeaker
C2 = Childutterance with age classclusterof belonging cluster
Child
Adult → Child(Replaced with cluster’s class): Target speech and estimated class
C*: Class decided by voting for each cluster
Evaluation criteria

Conventional

Proposed

Classification accuracy(Child, Adult, Elderly)

59 %

72 %

Mean absolute error± 10.9 years± 8.7 years

Fig.2 Visualization of output of age-estimation model(cited from [1]) (left: training data, right: evaluation data)

References[1] N. Tawara, H. Kamiyama, S. Kobashikawa, A. Ogawa, “Improving speaker-attribute estimation by voting based on speaker cluster
information,” Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing （ICASSP）, pp. 6594-598, May 2020.
[2] N. Tawara, H. Kamiyama, S. Kobashikawa, A. Ogawa, “Frame-level phoneme-invariant speaker feature extraction for text-independentspeaker recognition on extremely short utterances,” Reports of the autumn meeting the Acoustical Society of Japan., pp. 815-816, Sept.
2019.

Contact
Naohiro Tawara Email: cs-openhouse-ml@hco.ntt.co.jp
Signal Processing Research Group, Media Information Laboratory
オープンハウス 2020