The Comprehensive Guide: Influencing Factors In Speech Quality Assessment Using Crowdsourcing

Speech quality assessment plays a crucial role in various fields such as telecommunications, voice assistants, and audio processing. Accurate evaluation of speech quality is instrumental in improving communication systems, optimizing voice recognition algorithms, and enhancing user experiences. While traditional methods of speech quality evaluation have long existed, recent advancements in technology and the emergence of crowdsourcing have revolutionized the process.

The Power of Crowdsourcing

Crowdsourcing, as a concept, involves harnessing the power of a large group of people to achieve a common goal. When applied to speech quality assessment, crowdsourcing allows for the collection of subjective opinions from a diverse set of human listeners. By aggregating these subjective evaluations, more accurate insights into the perceived quality of speech can be obtained, taking into account different factors that influence human perception.

The Challenge of Subjectivity

Speech quality can be influenced by various factors, including background noise, voice intelligibility, clarity, interruptions, and latency, among others. However, assessing speech quality can be subjective, as it depends on the individual listener's preferences, cultural background, and personal experiences. Achieving a consensus in such subjective evaluations is a challenge but can be effectively addressed through crowdsourcing.

Influencing Factors in Speech Quality Assessment using Crowdsourcing

by Rafael Zequeira Jiménez (Kindle Edition)

5 out of 5

Language	:	English
File size	:	6619 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	239 pages

FREE

Influencing Factors in Speech Quality Assessment

To ensure accurate speech quality assessments using crowdsourcing, it is crucial to consider several key factors. These factors contribute significantly to shaping the overall user experience and can directly affect how speech quality is perceived. Let's explore some of the most influential factors below:

1. Background Noise

Background noise plays a substantial role in speech quality perception. High levels of noise can reduce the intelligibility of speech, making it challenging for listeners to understand the message. Crowdsourcing platforms need to account for background noise levels when assessing speech quality, as different noise environments can impact user satisfaction and the overall perceived quality of audio.

2. Voice Intelligibility

Voice intelligibility refers to the ability of listeners to understand and comprehend speech effectively. Factors such as pronunciation, articulation, and accent can influence voice intelligibility. Crowdsourcing platforms must consider the diverse linguistic backgrounds and accents of their participants to obtain accurate evaluations of voice intelligibility.

3. Clarity and Pronunciation

The clarity and pronunciation of speech are vital components in determining speech quality. Even if background noise is minimal and voice intelligibility is high, unclear pronunciation or ambiguous speech can negatively impact the perceived quality. Crowdsourcing platforms need to ensure that listeners pay close attention to these factors, as they greatly influence speech quality assessments.

4. Latency and Delay

Latency and delay can significantly affect speech quality, especially in real-time communication applications such as voice and video conferencing. Delayed responses or issues with synchronization can create a disruption in communication flow, leading to a decrease in perceived quality. Crowdsourcing platforms can address this by simulating real-time scenarios to obtain accurate evaluations of speech quality that reflect real-life experiences.

5. Contextual Relevance

The context in which speech is delivered greatly impacts its quality assessment. For example, speech in a quiet room may be perceived differently compared to speech in a bustling café. Crowdsourcing platforms should provide context to participants so that they can adequately evaluate the speech quality in relation to the given setting. This adds an extra layer of accuracy to the assessment process.

Challenges and Solutions

While crowdsourcing offers significant advantages in speech quality assessment, there are several challenges to consider. To ensure reliable and consistent results, platforms must carefully address these challenges using innovative solutions. Some of the common challenges and potential solutions include:

1. Quality Control

Maintaining quality control can be a challenge when dealing with a diverse pool of participants. To address this, platforms can implement rigorous screening processes to ensure that participants meet specific criteria. Additionally, random sampling of assessments and periodic quality checks can help maintain the accuracy and reliability of evaluations.

2. Sample Bias

Sample bias occurs when the composition of the participant pool does not reflect the intended target audience. To mitigate this, platforms can employ demographic filters to ensure a representative sample. This helps ensure that the evaluations obtained are more relevant and representative of the general population's perception of speech quality.

3. Gamification

Introducing elements of gamification can help maintain participant engagement and motivation. Platforms can implement leaderboard systems, reward systems, and friendly competition among participants to enhance their drive to provide accurate evaluations consistently. This approach ensures a continuous flow of assessments and contributes to the overall success of the crowdsourcing process.

In , crowdsourcing has emerged as a powerful tool in speech quality assessment, enabling more accurate evaluations by leveraging the diverse perspectives of a large number of participants. By considering influential factors such as background noise, voice intelligibility, clarity, latency, and contextual relevance, platforms can obtain comprehensive insights into speech quality. Challenges related to quality control, sample bias, and participant engagement can be effectively addressed through innovative solutions. Ultimately, the combination of crowdsourcing and careful consideration of influencing factors opens up new possibilities for enhancing speech quality assessments, leading to improved communication systems and enriched user experiences.

Influencing Factors in Speech Quality Assessment using Crowdsourcing

by Rafael Zequeira Jiménez (Kindle Edition)

5 out of 5

Language	:	English
File size	:	6619 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	239 pages

FREE

This book evaluates the impact of relevant factors affecting the results of speech quality assessment studies carried out in crowdsourcing. The author describes how these factors relate to the test structure, the effect of environmental background noise, and the influence of language differences. He details multiple user-centered studies that have been conducted to derive guidelines for reliable collection of speech quality scores in crowdsourcing. Specifically, different questions are addressed such as the optimal number of speech samples to include in a listening task, the influence of the environmental background noise in the speech quality ratings, as well as methods for classifying background noise from web audio recordings, or the impact of language proficiency in the user perception of speech quality. Ultimately, the results of these studies contributed to the definition of the ITU-T Recommendation P.808 that defines the guidelines to conduct speech quality studies in crowdsourcing.