A computing system for language-based service hailing, comprising: one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the computing system to: obtain a plurality of speech samples, each speech sample comprising one or more words spoken in a language; train a neural network model with the speech samples to obtain a trained model for determining languages of speeches; obtain a voice input; identify a language corresponding to the voice based at least on applying the trained model to the voice input; and communicate a message in the identified language, characterized in that, the neural network model comprises a convoluted neural network, CNN, and one or more Gated Recurrent Units, GRUs, wherein the CNN includes neuron layers and a fully connected layer, wherein the neuron layers are configured to process the speech samples, and the fully connected layer includes a plurality of fields and a plurality of field values, each of the plurality of fields corresponds to a language and each of the plurality of field values corresponds to a probability that the speech sample corresponds to the language of the field; and before obtaining the fully connected layer of the CNN, the one or more GRUs are applied to each channel output of the CNN to gate results of the neuron layers. one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the computing system to: obtain a plurality of speech samples, each speech sample comprising one or more words spoken in a language; train a neural network model with the speech samples to obtain a trained model for determining languages of speeches; obtain a voice input; identify a language corresponding to the voice based at least on applying the trained model to the voice input; and communicate a message in the identified language, characterized in that, the neural network model comprises a convoluted neural network, CNN, and one or more Gated Recurrent Units, GRUs, wherein the CNN includes neuron layers and a fully connected layer, wherein the neuron layers are configured to process the speech samples, and the fully connected layer includes a plurality of fields and a plurality of field values, each of the plurality of fields corresponds to a language and each of the plurality of field values corresponds to a probability that the speech sample corresponds to the language of the field; and before obtaining the fully connected layer of the CNN, the one or more GRUs are applied to each channel output of the CNN to gate results of the neuron layers. obtain a plurality of speech samples, each speech sample comprising one or more words spoken in a language; train a neural network model with the speech samples to obtain a trained model for determining languages of speeches; obtain a voice input; identify a language corresponding to the voice based at least on applying the trained model to the voice input; and communicate a message in the identified language, characterized in that, the neural network model comprises a convoluted neural network, CNN, and one or more Gated Recurrent Units, GRUs, wherein the CNN includes neuron layers and a fully connected layer, wherein the neuron layers are configured to process the speech samples, and the fully connected layer includes a plurality of fields and a plurality of field values, each of the plurality of fields corresponds to a language and each of the plurality of field values corresponds to a probability that the speech sample corresponds to the language of the field; and the neuron layers are configured to process the speech samples, and the fully connected layer includes a plurality of fields and a plurality of field values, each of the plurality of fields corresponds to a language and each of the plurality of field values corresponds to a probability that the speech sample corresponds to the language of the field; and before obtaining the fully connected layer of the CNN, the one or more GRUs are applied to each channel output of the CNN to gate results of the neuron layers.