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The issue is that short utterances that don’t meet...
# ask-ai
p
The issue is that short utterances that don’t meet interruption criteria are still transcribed and queued, causing turn misalignment in real time. This makes the bot feel out of sync, because the user hears their own short comment in one place, but the bot processes it as if it came later.
t
Thanks for reaching out, Bryner. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
Hey there! If you're running into issues with short utterances causing turn misalignment in real-time transcription, no worries — there are a few tweaks you can try in LiveKit to smooth things out: • Turn Detection Modes: Consider using modes like 
realtime_llm
or custom models such as 
EnglishModel
or 
MultilingualModel
for more accurate detection. It’s usually best to avoid relying only on 
stt
or 
vad
if they’re causing early turn detection. • Endpointing Delays: Try increasing 
min_endpointing_delay
(default is 0.4s) to give a bit more breathing room for short utterances. You can also adjust 
max_endpointing_delay
as needed. • Turn Detection Configuration: Setting 
min_interruption_duration
(default: 0.5s) helps define the minimum speech length required for interruptions. • Model-Specific Adjustments: If you’re using Cartesia TTS, enabling 
use_tts_aligned_transcript=True
can really help with better synchronization. Here’s a quick example configuration you might find handy:
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session = AgentSession(
    turn_detection=EnglishModel(),  # or MultilingualModel()
    min_endpointing_delay=0.6,
    max_endpointing_delay=4.0,
    use_tts_aligned_transcript=True
)
Give these a try! They should help reduce misalignment and make your bot feel more responsive. If you hit any snags or want to chat more about it, just let me know! Sources: tests/test_tokenizer.py
p
Filipino langauge does not seem to be supported by both lanuage
t
Hey there! Just wanted to share some info about Filipino language support in LiveKit, which varies depending on the component: • Speech-to-Text (STT): Good news! Filipino is supported by the Google STT plugin (
fil-PH
) and the Gladia STT plugin (
fil
), though it’s not available with the Spitch plugin. • Turn Detection: Filipino isn’t directly supported here. The supported languages include English, French, Spanish, and a few others, but Filipino isn’t on that list yet. If you want to use Filipino with turn detection, you can set things up using a multilingual model and specify the language code for STT like this:
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turn_detection=MultilingualModel(),
stt=gladia.STT(languages=["fil"])
Feel free to reach out if you want a hand setting this up or have any other questions! Sources: livekit-plugins/livekit-plugins-spitch/livekit/plugins/spitch/stt.py | Building the all-in-one platform for voice AI agents | Building the all-in-one platform for voice AI agents
p
Yes my stt openai supprots tl but dthe turndetection multilingaul is not listed tl there so do u recommend i still use it
t
Hey there! Even though the 
MultilingualModel
in LiveKit's turn detection doesn’t officially support Filipino, you can definitely still give it a try and experiment. It’s built to handle 13 languages, but sometimes it can work with others like Filipino depending on how you set things up. To get started, just make sure your STT plugin (like OpenAI) supports Filipino and set it up like this:
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session = AgentSession(
    stt=openai.STT(language="tl"),  # Filipino language support
    turn_detection=MultilingualModel(),
    # Other configurations
)
Performance might be a bit of a mixed bag, but this is a great way to see if it fits your needs. If you want, I’m here to help with any other questions or suggest some alternatives!
p
Issue: When a user speaks a short utterance during the bot’s speech that doesn’t meet the interruption thresholds (min_interruption_words=4, min_interruption_duration=1.0), the bot continues talking as expected, but that short utterance is queued and inserted into the next turn, making the conversation feel out of sync. This happens even though discard_audio_if_uninterruptible defaults to True, which I thought would drop that audio instead of queuing it.
t
It sounds like the 
discard_audio_if_uninterruptible
setting isn’t catching those short utterances, which can sometimes happen because of timing or threshold settings. Here’s a friendly step-by-step to help fix that: • Adjust Minimum Thresholds: Try lowering 
min_interruption_duration
to something like 0.1s and set 
min_interruption_words
to 0. This helps make sure even quick interruptions get noticed:
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session = AgentSession(
    min_interruption_duration=0.1,
    min_interruption_words=0,
    discard_audio_if_uninterruptible=True,
    allow_interruptions=True
)
• Verify Speech Configuration: Double-check that uninterruptible speech is set up correctly by using 
allow_interruptions=False
during your bot’s responses. • Test and Validate: Run some test sessions with these new settings and keep an eye on the logs to see how speech state changes. This can give you a clearer picture of what’s happening. If things still feel off, you might want to explore some custom audio management logic or tweak turn detection to be a bit more sensitive. And of course, I’m here if you want to chat more about these settings or need a hand troubleshooting! Sources: livekit-agents/livekit/agents/voice/speech_handle.py
p
There is a reason why i set minintertuptjblewords to 4 and mininterduration to 1 but when the words below 4 are captured it is queued to the next turn maiing the llm also capture that text where i want it discarded
t
Hey! To make sure those short utterances below your interruption thresholds don’t end up getting queued, you can tweak your setup a bit to have them discarded right away: • Interruption Parameters: Keep 
min_interruption_words=4
and 
min_interruption_duration=1.0
as you have them, but just double-check these thresholds are applied consistently. • Custom Logic: You can use callbacks like 
on_user_turn_completed
to filter out utterances that don’t meet your thresholds before they get queued. • Example Configuration:
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session = AgentSession(
    options=VoiceOptions(
        min_interruption_duration=1.0,
        min_interruption_words=4,
        discard_audio_if_uninterruptible=True
    )
)
This way, any utterances shorter than your thresholds will be discarded instead of piling up in the queue. If you want, I’m here to help you set this up or answer any questions!
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