Improving Speaker Recognition by Biometric Voice Deconstruction

Mazaira-Fernández, Luis Miguel; Álvarez-Marquina, Agustín; Gómez-Vilda, Pedro

doi:10.3389/fbioe.2015.00126

Cited by 16 publications

(14 citation statements)

References 44 publications

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“…Researchers have attempted to find alternative features. For example, it has been found that voice sourcerelated features improve speaker recognition systems by providing information that complements conventional cepstral features [4,5,6]. Das et al also reported that features extracted from the voice source signal outperform MFCCs in ASV with test utterances shorter than 3 seconds [7].…”

Section: Introductionmentioning

confidence: 99%

Using Voice Quality Features to Improve Short-Utterance, Text-Independent Speaker Verification Systems

et al. 2017

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Due to within-speaker variability in phonetic content and/or speaking style, the performance of automatic speaker verification (ASV) systems degrades especially when the enrollment and test utterances are short. This study examines how different types of variability influence performance of ASV systems. Speech samples (< 2 sec) from the UCLA Speaker Variability Database containing 5 different read sentences by 200 speakers were used to study content variability. Other samples (about 5 sec) that contained speech directed towards pets, characterized by exaggerated prosody, were used to analyze style variability. Using the i-vector/PLDA framework, the ASV system error rate with MFCCs had a relative increase of at least 265% and 730% in content-mismatched and style-mismatched trials, respectively. A set of features that represents voice quality (F0, F1, F2, F3, H1-H2, H2-H4, H4-H2k, A1, A2, A3, and CPP) was also used. Using score fusion with MFCCs, all conditions saw decreases in error rates. In addition, using the NIST SRE10 database, score fusion provided relative improvements of 11.78% for 5-second utterances, 12.41% for 10-second utterances, and a small improvement for long utterances (about 5 min). These results suggest that voice quality features can improve short-utterance text-independent ASV system performance.

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Section: Introductionmentioning

confidence: 99%

Using Voice Quality Features to Improve Short-Utterance, Text-Independent Speaker Verification Systems

et al. 2017

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“…For example, Espy-Wilson et al used eight acoustic parameters consisting of both voice source and vocal tract features [11]. Mazaira-Fernandez et al separated voice source from vocal tract information, and they combined cepstral coefficients from those two estimates [12]. These studies showed the effectiveness of voice source information with promising results, but such information still has not been utilized extensively in ASpR.…”

Section: Introductionmentioning

confidence: 99%

Speaker Identity and Voice Quality: Modeling Human Responses and Automatic Speaker Recognition

Park

Sigouin²,

Kreiman

et al. 2016

Interspeech 2016

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Despite recent breakthroughs in automatic speaker recognition (ASpR), system performance still degrades when utterances are short and/or when within-speaker variability is large. This study used short test utterances (2-3sec) to investigate the effect of within-speaker variability on state-of-the-art ASpR system performance. A subset of a newly-developed UCLA database is used, which contains multiple speech tasks per speaker. The short utterances combined with a speaking-style mismatch between read sentences and spontaneous affective speech degraded system performance, for 25 female speakers, by 36%. Because humans are more robust to utterance length or withinspeaker variability, understanding human perception might benefit ASpR systems. Perception experiments were conducted with recorded read sentences from 3 female speakers, and a model is proposed to predict the perceptual dissimilarity between tokens. Results showed that a set of voice quality features including F0, F1, F2, F3, H1*-H2*, H2*-H4*, H4*-H2k*, H2k*-H5k, and CPP provides information that complements MFCCs. By fusing the feature set with MFCCs, human response prediction RMS error was .12, which represents a 12% relative error reduction compared to using MFCCs alone. In ASpR experiments with short utterances from 50 speakers, the voice quality feature set decreased the error rate by 11% when fused with MFCCs.

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“…The most common features used in audio recognition are the Mel-frequency Cepstral Coefficients (MFCCs). In the speech processing domain, the first thirteen MFCC values have been verified to be particularly pertinent due to their approximate separation of the glottal excitation from the vocal tract [6]. The MFCCs are perceptual features computed from the short-term Fourier transform.…”

Section: A Audio Features Representationsmentioning

confidence: 99%

Open Set Audio Recognition for Multi-Class Classification With Rejection

Jleed

Bouchard

2020

IEEE Access

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Most supervised audio recognition systems developed to this point have used a testing set which includes the same categories as the training set database. Such systems are called closed-set recognition (CSR). However, audio recognition in real applications can be more complicated, where the datasets can be dynamic, and novel categories can ceaselessly be detected. Hence, in practice, the usual methods will assign to these novel classes labels which are often incorrect. This work aims to investigate audio open-set recognition (OSR) suitable for multi-classes classification recognition, with a rejection option for classes never seen by the system. A probabilistic calibration of a support vector machine classifier is utilized and formulated under the open-set scenario. For this, it is proposed to apply a threshold technique called peak side ratio (PSR) to the audio recognition task. A candidate label is first examined by a Platt-calibrated support vector machine (SVM) to produce posterior probabilities. The PSR is then used to characterize the distribution of posterior probabilities values. This process helps to determine a threshold in order to reject or accept a particular class. Our proposed method is evaluated on different variations of open sets, using wellknown metrics. Experimental results reveal that our proposed method outperforms previous OSR approaches over a wide range of openness values.

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Improving Speaker Recognition by Biometric Voice Deconstruction

Cited by 16 publications

References 44 publications

Using Voice Quality Features to Improve Short-Utterance, Text-Independent Speaker Verification Systems

Using Voice Quality Features to Improve Short-Utterance, Text-Independent Speaker Verification Systems

Speaker Identity and Voice Quality: Modeling Human Responses and Automatic Speaker Recognition

Open Set Audio Recognition for Multi-Class Classification With Rejection

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