#Japan #Tokyo #AI Development #Qlean Dataset #Visual Bank

Visual Bank's New AI Dataset for Complaint Handling

Visual Bank Inc., based in Shibuya, Tokyo, led by CEO Masanori Nagai, has announced the launch of its upgraded dataset: the Japanese Complaint Response, Speaker Separation, and Voice Dialogue Dataset. This addition is part of the Qlean Dataset, a versatile AI training data solution that supports both commercial and research domains in AI development. With a commitment to expanding its unique data offerings, Visual Bank aims to streamline the data acquisition process for artificial intelligence projects.

Expansion of the Qlean Dataset

The Qlean Dataset includes an original lineup of data available for commercial use known as Data Recipes. This feature allows users to customize and combine data materials according to their needs, precision, and delivery timelines. The dataset is flexible, comprising some annotated and unannotated data, alongside options for customization and expansion based on specific requirements. Collaborations with entities like the Chiba Lotte Marines and Toyo Keizai Inc., coupled with international networks and new recordings, support the continuous expansion of their dataset offerings.

This initiative significantly reduces the burden of data collection and organization for AI development environments, consequently accelerating project timelines.

Overview of the New Data Set

The newly launched Japanese Complaint Response Dataset includes:

• - Realistic Conversation Simulations: The dataset features scripted audio data representing conversations between operators and customers.
• - Speaker Information: The recordings consist of a female operator in her 40s and customers, including females in their 30s and males in their 20s.
• - Studio Recorded Quality: Audio was captured in a controlled studio environment using high-quality microphones such as the Neumann U87 for the operator and the Shure SM58 for customers.
• - Format and Quality: The audio files are in .wav format with 8bit depth at a sample rate of 48,000Hz, encompassing a total of six files of mixed audio content (operator, customer, and a combined track).
• - Customization Options: As the provided dataset represents samples, further recordings can be tailored to meet specific project requirements.

For more details on this dataset, visit: Japanese Complaint Response Dataset.

Use Cases for the New Dataset

• - Customer Harassment Detection: By training models on actual complaint audio that includes abusive or intimidating language, this dataset can enhance the accuracy of harassment detection systems, helping to protect operators and establish response guidelines.
• - HR Monitoring of Operator Performance: Utilizing metrics from voice tone, pace, and hesitations, organizations can identify stress trends and performance levels, aiding in workforce management and training strategies.
• - Training for Speaker Separation Technology: The dataset provides three audio types (operator, customer, and mixed) for training and evaluating speaker separation and recognition algorithms in realistic scenarios.
• - AI Development for Call Center Scoring Systems: This dataset is suited for developing AI models that quantitatively assess apology expressions, proper use of honorifics, and response timing—valuable for training new employees and improving service quality.
• - Coaching Services for Communication Skills: The dataset offers insights into difficult communication situations commonly faced in customer complaints, enhancing coaching and interpersonal support services.
• - Enhancing Emotional Understanding in AI: The dataset reflects real-world conversations containing various emotional tones (anger, frustration, confusion), which can be instrumental in improving AI's response accuracy and emotional adaptability.

Key Features of the Qlean Dataset

• - Research and Commercial Utilization: Qlean Dataset ensures that datasets are compliant with privacy policies across different countries, providing peace of mind for research and commercial applications.
• - Quick and Cost-effective Data Provisioning: The unique Data Recipe model allows for speedy data acquisition while minimizing initial investment requirements.
• - Customization Capabilities: For specific data needs not found in the Data Recipes lineup, Qlean Dataset can provide tailored datasets, ensuring that bespoke project requirements are met effectively.

For inquiries regarding the Qlean Dataset, please visit the Qlean Dataset Contact Form or access their service site at Qlean Dataset Service Site.

Company Background

Visual Bank Inc. is a startup that has adopted the GENIAC initiative, aiming to maximize AI development capabilities by constructing and providing a next-generation data infrastructure. Their mission is to unlock the potential of all data, furthering work in AI use cases through projects like THE PEN alongside its 100% subsidiary, Amana Images, which specializes in AI training datasets.

CEO: Masanori Nagai
Headquarters: 5-3-23 Kojimachi, Chiyoda Ward, Tokyo, in the NTV Yotsuya Building WeWork
Visual Bank Website: Visual Bank
Amana Images Website: Amana Images