From Speech Recognition to Voice Agents: Training Data for the Next Generation of Voice AI

Breakthroughs in AI technology over the last decade have driven the evolution of Voice AI models.

From Automatic Speech Recognition (ASR) and Spoken Language Understanding (SLU) to advanced Voice Agents today, voice technology now enables not only speech transcription and semantic understanding, but also continuous multi-turn conversations with humans and various complex operations.

Also, Voice is becoming not just an input method for communicating with machines, but an important interface for interacting with AI models.

Along with the evolution of models, however, another process is taking place: **training data is becoming more and more sophisticated as well.

While in the past speech models were mainly trained to recognize what was said, nowadays they have to understand and participate in human conversations. And as Voice Agents move from simple conversations to more advanced, natural, continuous, and real-time interactions, training data is evolving from Speech Data to much more sophisticated Conversational Speech Data.

Evolution of Speech Models Is Accompanied by Evolution of Training Data

Various generations of speech models require different types of data for their training.

Early ASR models concentrated mainly on improving speech recognition. The corresponding training data consisted of read speech, transcriptions, and coverage across various languages and accents. In this case, the main question was quite clear: What was said? Therefore, data development concentrated on speech diversity and transcription quality.

With the appearance of Speech LLMs and other large speech models, contextual and semantic understanding of speech became more and more important. Continuous conversations, speaker information, contextual relations, and intentions are becoming more important than separate utterances. Training data has to provide the information needed to understand the context of the conversation as a whole rather than separate sentences.

Voice Agents are bringing this trend even further. They require not only an understanding of spoken content, but also participation in the whole conversation—following the context, maintaining conversational continuity, managing turn-taking, handling interruptions and overlapping speech, and responding to users in real time.

Training data is moving from Speech to Conversation.

As a consequence, speech training data is becoming not just about recording speech, but rather about recording how people really communicate.

High-Quality Speech Data Is Moving From Speech to Conversation

The rise of Voice Agents doesn’t make conventional speech data less important. Instead, it is redefining what high-quality speech data should contain.

Compared to conventional ASR training, next-generation Voice AI requires not only natural and continuous conversations, but also the capture of natural interaction patterns like pauses, interruptions, overlapping speech, confirmations, follow-ups, and even changes in emotion. However, these interaction patterns are difficult to capture through standardized read speech or isolated recordings. Yet, they may significantly affect the way a Voice Agent interacts with the user.

Here comes into play the importance of** full-duplex, dual-channel conversational speech data.** Preserving each speaker on an independent audio channel allows conversational patterns like turn-taking, overlapping speech, and interruptions to be captured much more clearly.

At the same time, speech data starts requiring richer annotation. Transcription, speaker diarization, emotion annotation, intent annotation, timestamps, and turn boundaries allow models to understand not only what was said, but also who was speaking, when they were speaking, and how the conversation was developing.

With the global expansion of Voice AI, multilingual, multi-accent, and multi-scenario coverage becomes increasingly important. Customer service, workplace assistants, and smart device applications across different languages, markets, and environments require diverse and representative Conversational Speech Data.

Therefore, high-quality speech data is no longer just about more audio and more accurate transcriptions. Rather, it is increasingly important to capture the complexity of natural human interaction.

Developing Conversational Speech Data for the Next Generation of Voice AI

In order to support the new training data needs of Voice AI, Nexdata continues developing global, multilingual, and multi-scenario Conversational Speech Data capacity for speech recognition, Speech LLMs, Voice Agents, and other Voice AI applications.

Nexdata has created global speech data collection capacity covering more than 200 languages, offering multilingual speech collection, natural conversational speech collection, speech annotation, and Bespoke data production services for various phases of model development.

For natural conversational speech, Nexdata continues developing full-duplex, dual-channel speech data resources covering more than 30 languages, including over 2 million hours of English dual-channel customer service conversations. Thanks to independent speaker channels and complete conversation capture, such datasets can capture turn-taking, overlapping speech, interruptions, and other natural interaction patterns, thus supporting the development of Voice Agents, multi-turn speech models, and Speech Foundation Models.

Nexdata also provides multidimensional speech annotation, including transcription, speaker diarization, emotion annotation, intent annotation, timestamps, and other conversational metadata. Bespoke speech data collection and production are available for various scenarios such as customer service, workplace applications, education, and smart devices.

Through Off-the-Shelf datasets, Bespoke data collection, and speech annotation, Nexdata is helping develop Conversational Speech Data for various models, training phases, languages, and scenarios.

[Full-Duplex Speech Data]

[Natural Conversational Speech Data]

[TTS Speech Data]

From Speech Recognition to Voice Agents: Training Data Is Entering a New Phase

For many years, one of the key purposes of Voice AI was to allow machines to accurately “understand” what people said. But with the arrival of Voice Agents, models started to learn something much more complicated: how to participate in natural, continuous, real-time conversations.

Training data is changing accordingly. Speech Data created primarily for recognition is transforming into Conversational Speech Data that captures the dynamics of human interaction. The importance of speech data is determined not only by size and transcription accuracy, but also by interaction coverage, languages, scenarios, and other complex conversational behaviors.

From speech recognition to Voice Agents, not only are models changing—the data that helps them learn human interaction is changing as well.

Nexdata will continue developing its global Conversational Speech Data capacity through Off-the-Shelf datasets, Bespoke data collection, speech annotation, and multilingual data resources, thus providing high-quality training data for the next generation of Voice AI.