I'm using the phone integration in DialogFlow.
I have a scenario where there is a continuous speech for about 40-50 seconds.
I'm trying to match an intent of one sentence (5 to 7 words) inside this speech section. It looks like DialogFlow will not try to match the intent until it gets a reasonable pause in the speech.
Is there a way to get a more "real time" response?
Or to set the silence detection that breaks between intent recognitions to a smaller value?
Any other strategy?
Related
Hi so I have a problem.
In Dialogflow, when I get a response to end the chat, I would like to ask the user for ratings.
so I've created 2 intents, "endchat" and "endchat2."
They both have the same training phrases, but it appears only endchat2 is being used (the most recently created intent)
How do I ensure that the chatbot randomly chooses an intent after a given response, instead of only using one intent? They have the same training phrases.
An alternate idea is in the attachments. The problem lies that I want the custom payload to only to appear after one of the text responses, (that being text response #1,) but not appear, if the chatbot decides to use text response #2. This is the reason I decide to make two separate intents, but it looks like that's not helping out because the bot is only using one intent.
Remember, Intents represent what the user says and does and not how you respond to that. So there is no way to "randomly choose an Intent" to use to respond.
What you can do, however, is setup a webhook for that Intent and determine how you wish to respond to what the user says. In some cases, you can thank them and end the conversation, while in others you can thank them, ask them the followup question, and set a Context so you can expect their reply.
Having the same / similar training phrase in multiple intents is an anti-pattern of bot design. Ultimately this confuses the bot and it leads to undefined behavior.
This should also trigger an warning in "Validation" with something like "Multiple intents share training phrases which are too similar:..." on the intents.
In my application, I need to record a conversation between people and there's no room in the physical workflow to take a 20 second sample of each person's voice for the purpose of training the recognizer, nor to ask each person to read a canned passphrase for training. But without doing that, as far as I can tell, there's no way to get speaker identification.
Is there any way to just record, say, 5 people speaking and have the recognizer automatically classify returned text as belonging to one of the 5 distinct people, without previous training?
(For what it's worth, IBM Watson can do this, although it doesn't do it very accurately, in my testing.)
If I understand your question right then Conversation Transcription should be a solution for your scenario, as it will show the speakers as Speaker[x] and iterate for each new speaker, if you don't generate user profiles beforehand.
User voice samples are optional. Without this input, the transcription
will show different speakers, but shown as "Speaker1", "Speaker2",
etc. instead of recognizing as pre-enrolled specific speaker names.
You can get started with the real-time conversation transcription quickstart.
Microsoft Conversation Transcription which is in Preview, now targeting to microphone array device. So the input recording should be recorded by a microphone array. If your recordings are from common microphone, it may not work and you need special configuration. You can also try Batch diarization which support offline transcription with diarizing 2 speakers for now, it will support 2+ speaker very soon, probably in this month.
I am using Dialogflow to create a chatbot that can be used on google assistant. However the speech recognition often mis-recognizes the intended word. Example, when I say the word "seal", it recognizes the spoken word wrongly as "shield".
Is there any way to "train" or make google assistant better recognize a word?
If you have a limited amount of words that you would like to improve upon, then using Dialogflow's entities would be an option. For instance, if you are trying to recognize certain animals. You can create a set of animals as entities and set the intent to look for an animal entity in the user input.
Besides this option I don't know of any other things to improve the speech itself, you could train Dialogflow to map both "seal" and "shield" to your desired intent, but that doesn't change the actual word, it will still be shield.
For any other improvements to the speech recognition, I'm afraid you will have to wait for updates from Google to their algorithms.
Just found out there is a new beta function in dialogflow that should help.
https://cloud.google.com/dialogflow/docs/speech-adaptation
Edit:
However does not work with Actions on google.
I've made an intent to detect user's answer when they say, for example "when shop is closed" where "closed" is an entity.
When I give the input exactly the same as my training phrase, "when shop is closed", everything is working as expected and and dialogflow correctly detected the intent and the entity value (as shown in the second screenshot).
However, when I input a slight variant to the training phrase, by adding extra words "I think" in front of the sentence, dialogflow still correctly detected the intent, but however this time the parameter value is empty. (as shown in first screenshot)
I will need the value to be detected in both cases, and can't figure out why this is happening.
Screenshot 1
Screenshot 2
Google has published best practices for conversation design here, which should help:
https://developers.google.com/actions/assistant/basics
In this case, have you tried adding, "When is the shop closed?" as a training phrase? Clarifying verb tenses and sentence structure might help Dialogflow correctly identify the parameters you're hoping to extract from a user's given intent.
i need to develop a chat bot on azure for user interaction i have used LUIS and now want the bot to perform analyze the chat and suggest user the necessary changes. So, should i use text analytic API for it and does LUIS and text analytic API can be used together?
Text analytics can determine sentiments, extract key phrases and detect the language used. If you want to find the intent of the user or extract entities from a text, you can use LUIS.
For "The hotel is the worst ever" a sentiment analysis can tell that the sentiment is negative. For the same sentence key phrase extraction extracts the key words/phrases: "hotel, worst", without any interpretation of the meaning or context.
For "Turn on the yellow light", LUIS can be trained to extract intent (Operate Light) and entities (Action: turn on, Object: Yellow Light) with a meaning and a context.
Text Analytics and LUIS expose separate APIs that just takes texts as input, so they can be used independently of each other. They have no integrations built in between them, so that's up to the consumer to implement.