u/Helpful_Actuator9790

Working on conversational voice systems internally and we keep running into the same bottleneck where most public speech/TTS datasets still feel very “lab-like” compared to production environments.

A lot of the common datasets are:
- clean recordings
- isolated speakers
- controlled pacing/prosody
- low interruption overlap
- limited emotional variance
- minimal telecom degradation
- highly structured speech

which varies pretty drastically from real deployments.

We’ve been trying to find stronger datasets around:
- emotionally dynamic conversations
- multilingual / code-switching dialogue
- interruption-heavy interactions
- degraded VoIP/mobile audio
- difficult ASR/TTS environments
- long-form conversational drift
- overlapping speakers
- real customer support style conversations
- retention / escalation scenarios

Any recommendations on where to find datasets like these would be appreciated.

Feels like most public datasets still underrepresent the kinds of conversational conditions voice systems actually face once they hit production traffic.

Working on agent systems internally and we keep running into the same issue where most public datasets/evals still feel much cleaner and more controlled than real production environments.

A lot of the common datasets and benchmarks are:
- short interactions
- clean tool responses
- predictable workflows
- well-formed user inputs
- isolated tasks
- minimal state drift
- low ambiguity / low interruption scenarios

which ends up being pretty different from what deployed agent systems actually face.

We’ve been trying to find stronger datasets around:
- multi-step workflows with long-running state
- tool failures / partial responses
- conflicting tool outputs
- interruption-heavy user behavior
- ambiguous or underspecified requests
- retries / recovery scenarios
- long conversational drift over time
- agents operating under degraded conditions
- edge cases that only appear after extended interaction chains

Any recommendations on where to find datasets like these would be appreciated.

Feels like most public agent datasets still underrepresent the kinds of messy interaction patterns systems actually face once they hit production traffic.

Working on computer vision systems internally and we keep running into the same bottleneck where most public datasets still feel much cleaner and more controlled than real deployment environments.

A lot of the common datasets are:
- stable lighting
- fixed camera angles
- minimal occlusion
- low motion blur
- limited environmental variability
- clean object separation
- highly curated scenes

which ends up being pretty different from what production systems actually see.

We’ve been trying to find stronger datasets around:
- crowded / heavy occlusion environments
- difficult lighting and glare conditions
- motion blur and fast-moving objects
- low-quality CCTV / mobile footage
- weather variability
- long-form tracking scenarios
- temporal consistency issues across video sequences
- edge cases that only appear in real deployments
- overlapping objects and dense scenes

Any recommendations on where to find datasets like these would be appreciated.

Already tried Kaggle and a few others but it feels like most public CV datasets still underrepresent the kinds of messy real-world conditions the systems actually face while deployed.

Working on conversational/voice systems internally and we keep running into the same bottleneck where most public speech datasets still feel very “lab-like” compared to production environments.

A lot of the common datasets are:
- clean recordings
- isolated speakers
- minimal interruption overlap
- low emotional variance
- limited telecom degradation
- highly structured conversations

which vary drastically from real deployments:

We’ve been trying to find stronger datasets around:
- customer support conversations
- multilingual support calls
- emotionally escalated interactions
- difficult ASR environments
- telecom degradation
- retention/escalation scenarios
- interruption-heavy dialogue
- and long-form conversational drift.

Any recommendations on where to find datasets like these would be appreciated!

Feels like current public datasets still underrepresent the actual conditions most conversational systems face once they hit production traffic.