HeightCeleb: Augmenting VoxCeleb with Potentially Inaccurate Speaker Height Data

Khái niệm cốt lõi

Researchers introduce HeightCeleb, a dataset augmenting VoxCeleb with speaker height information, aiming to facilitate research on height estimation from speech despite potential inaccuracies in the collected data.

Tóm tắt

Bibliographic Information:

Kacprzak, S., & Kowalczyk, K. (2024). HeightCeleb--an enrichment of VoxCeleb dataset with speaker height information. arXiv preprint arXiv:2410.12668.

Research Objective:

This paper introduces HeightCeleb, a new dataset that adds speaker height information to the VoxCeleb dataset, aiming to address the lack of freely available, large-scale datasets for speaker height estimation research.

Methodology:

The researchers collected height information for 1251 speakers in the VoxCeleb dataset by querying Google Search, IMDB, and celebheights.com. They then analyzed the collected data and compared its statistical properties (mean, median, standard deviation, minimum, maximum) to existing datasets with height annotations, namely TIMIT and NISP. Finally, they demonstrated the potential of HeightCeleb by training simple regression models (MLR and PLSR) on the dataset using pre-trained ECAPA-TDNN speaker embeddings and evaluating their performance on TIMIT and HeightCeleb test sets.

Key Findings:

• HeightCeleb provides a significantly larger and more gender-balanced dataset for speaker height estimation research compared to existing freely available options.
• Training regression models on HeightCeleb with pre-trained speaker embeddings achieves comparable results to state-of-the-art methods on the TIMIT dataset.
• The study highlights the potential inaccuracies in the collected height data, emphasizing the need for careful consideration and further research on data reliability.

Main Conclusions:

HeightCeleb serves as a valuable resource for advancing research on speaker height estimation from speech, despite potential limitations in data accuracy. The authors encourage the development of more robust height estimation methods and emphasize the importance of evaluating error distributions beyond simple metrics like MAE and RMSE.

Significance:

This research contributes a valuable resource to the field of speaker recognition and speech processing by providing a large-scale dataset for speaker height estimation. It also highlights the challenges and considerations associated with collecting and utilizing potentially inaccurate data for research purposes.

Limitations and Future Research:

The study acknowledges the potential inaccuracies in the collected height data and suggests further research on improving data reliability. Future work could explore more sophisticated height estimation models and evaluate their performance on a gold standard dataset with precise height measurements. Additionally, investigating the ethical implications of using estimated personal attributes like height is crucial.

Thống kê

VoxCeleb contains speech of 1251 celebrities.
TIMIT comprises recordings of 630 individuals (438 male, 192 female).
NISP includes data from 345 speakers.
HeightCeleb contains over 153,000 utterances.
ECAPA-TDNN embeddings have a dimensionality of 192.
The gender classifier achieved 99.2% accuracy on the TIMIT test set.
The probability of predicting height within a 2 cm range increased from 19% to 29% for male speakers and from 29% to 30% for female speakers using the PLSR model.

Trích dẫn

"Information about the height of a speaker is relevant to voice forensics, surveillance, and automatic speaker profiling."
"A major problem encountered when investigating methods to determine speaker’s height from voice is the lack of datasets with height annotations."
"The height data represents estimates, with difficult to determine errors."
"We believe that any potential inaccuracies are still within a range that is sufficient for many practical applications, and the need for more precise height estimates would actually be very rare."

Thông tin chi tiết chính được chắt lọc từ

HeightCeleb -- an enrichment of VoxCeleb dataset with speaker height information

by Stan... lúc arxiv.org 10-17-2024

https://arxiv.org/pdf/2410.12668.pdf

Yêu cầu sâu hơn

How can the accuracy and reliability of speaker height data be improved in future datasets, considering the limitations of self-reported and publicly sourced information?

While self-reported and publicly sourced height information offer a starting point for datasets like HeightCeleb, improving accuracy and reliability in future datasets requires addressing inherent limitations. Here's how:

Controlled Data Collection: Transitioning from opportunistic data scraping to controlled data collection can significantly enhance data quality.

Direct Measurements: Incorporating direct height measurements, obtained under standardized conditions, can serve as a reliable ground truth. This could involve collaborations with research institutions or organizations with access to diverse populations.
Standardized Questionnaires: For self-reported data, employing standardized questionnaires that minimize ambiguity and bias can improve accuracy. Questions should be phrased to elicit precise responses, potentially including visual aids for reference points.

Data Source Validation: Relying on multiple, independent sources and implementing validation mechanisms can mitigate errors.

Cross-Verification: Cross-referencing height information from multiple reputable sources (e.g., official biographies, medical records with consent) can help identify and rectify discrepancies.
Image Analysis: Leveraging image analysis techniques on publicly available images of individuals could provide independent height estimates. By analyzing images with known reference points, algorithms can estimate height with reasonable accuracy.

Addressing Temporal Variations: Recognizing that height can fluctuate throughout the day and over a person's lifetime necessitates accounting for temporal factors.

Time of Recording: Documenting the time of day when speech recordings are made, especially for datasets involving direct measurements, allows for adjustments related to diurnal variations in height.
Age at Recording: Collecting and providing information about the speaker's age at the time of recording allows researchers to account for age-related height changes.

Transparency and Metadata: Providing comprehensive metadata and transparency regarding data sources and collection methods is crucial for researchers to understand limitations and potential biases.

Confidence Scores: Assigning confidence scores to height data points based on the source and validation methods used can indicate the reliability of each entry.
Data Source Documentation: Thoroughly documenting the origin of each height data point, including specific websites, databases, or measurement procedures, enables researchers to assess potential biases.

By implementing these strategies, future datasets can achieve higher accuracy and reliability, fostering more robust research on speaker height estimation from speech.

Could other speech features, beyond those captured in pre-trained speaker embeddings, be leveraged to improve the accuracy of height estimation models?

While pre-trained speaker embeddings, like those from ECAPA-TDNN, capture a wealth of speaker-specific information, incorporating additional speech features could potentially enhance height estimation accuracy. Here are some promising avenues:

Formant Frequencies: Formant frequencies, resonant frequencies of the vocal tract, are known to correlate with vocal tract length, which in turn is influenced by height.

Formant Extraction: Extracting and analyzing formant frequencies, particularly the first three formants (F1, F2, F3), can provide valuable cues for height estimation models.
Dynamic Formant Trajectories: Examining the dynamic trajectories of formants over time, rather than just static values, might offer more nuanced insights into vocal tract shape and size.

Spectral Characteristics: The overall spectral distribution of speech signals can also reflect vocal tract characteristics related to height.

Spectral Tilt: Spectral tilt, a measure of the balance between low and high frequencies, can be indicative of vocal tract length.
Spectral Moments: Statistical moments of the speech spectrum, such as skewness and kurtosis, can capture variations in spectral shape associated with different vocal tract sizes.

Prosodic Features: Prosodic features, encompassing intonation, rhythm, and stress patterns, might indirectly relate to height-related differences in lung capacity and vocal fold characteristics.

Pitch Range: The range of fundamental frequency (pitch) a speaker uses could be subtly influenced by lung capacity, which can be weakly correlated with height.
Speech Rate: While not a direct indicator, speech rate variations might be influenced by physiological factors related to height, although this relationship is likely complex and speaker-dependent.

Voice Quality Features: Subtle variations in voice quality, often reflecting anatomical differences in the vocal apparatus, could provide additional cues.

Jitter and Shimmer: These measures of perturbation in pitch and amplitude, respectively, might be subtly influenced by vocal fold size and tension, potentially correlating with height.

Integrating these features with existing speaker embeddings could lead to more accurate height estimation models. However, it's crucial to acknowledge that the relationship between these features and height is not always straightforward and can be influenced by factors like age, gender, and ethnicity.

What are the ethical implications of developing and deploying technologies that infer personal attributes like height from speech, and how can these concerns be addressed responsibly?

Developing technologies that infer personal attributes like height from speech raises significant ethical concerns that must be carefully considered and addressed responsibly:

Privacy Violation: Inferring sensitive personal information from speech, often without explicit consent, can be perceived as a privacy violation.

Data Security: Robust data security measures are paramount to prevent unauthorized access to speech data and inferred attributes, ensuring that such information is not misused.
Purpose Limitation: Clearly defining and adhering to strict purpose limitations for using height estimation technology is crucial. It should only be employed for specific, legitimate purposes with appropriate safeguards.

Discrimination and Bias: Height estimation models trained on biased data can perpetuate and amplify existing societal biases, leading to unfair or discriminatory outcomes.

Dataset Diversity: Ensuring diverse and representative datasets during model training is essential to mitigate bias and promote fairness in height estimation.
Bias Auditing: Regularly auditing models for bias and implementing mechanisms to detect and correct for discriminatory outcomes is crucial for responsible deployment.

Lack of Transparency and Explainability: The opacity of some AI models makes it challenging to understand how height inferences are made, potentially leading to mistrust and hindering accountability.

Explainable AI (XAI): Employing XAI techniques to provide insights into the decision-making process of height estimation models can enhance transparency and build trust.
Clear Communication: Communicating clearly how the technology works, its limitations, and potential biases to users and stakeholders is essential for fostering informed consent and responsible use.

Potential for Misuse: Like any technology, height estimation from speech can be misused for malicious purposes, such as profiling, surveillance, or discrimination.

Ethical Guidelines and Regulations: Establishing clear ethical guidelines and regulations governing the development and deployment of such technologies is crucial to prevent misuse.
Public Discourse: Fostering open public discourse involving ethicists, researchers, policymakers, and the public is essential to shape responsible innovation and establish appropriate safeguards.

Addressing these ethical implications requires a proactive and multifaceted approach. By prioritizing privacy, fairness, transparency, and accountability throughout the entire lifecycle of these technologies, we can strive to harness their potential while mitigating risks and ensuring responsible use.