Affective computing is the area of Artificial Intillegence studies which focuses on the design andrndevelopment of intelligent devices which can perceive, process and synthesize human emotions.rnHumans can interpret emotions in a number of different ways, for example, processing spokenrnutterances, non-verbal cues, facial expressions and also written communication. Changes in ourrnnervous system indirectly alter spoken utterances which makes it possible for people to perceivernhow others feel by listening to them speak. These changes can also be interpreted by machinesrnthrough the extraction of speech features. The field of speech emotion recognition (SER) takesrnadvantage of this capability and has subsequently offered many approaches to recognize affect inrnspoken utterances.rnThe majority of state of the art SER systems employ complex statistical algorithms to model thernrelationship between acoustic parameters extracted from spoken language. Studies also show thatrnphrases, word senses and syntactic relations that convey linguistic attributes of a language playrnan important role in enhancing the prediction rates. Our research focuses on this problem ofrnrecognizing affect in spoken utterances and offers a contribution to state of the art systems withrnlinguistic knowledge to enhance its efficiency instead of relying only on speech utterances. Inrnthis work, speech emotion recognition system is developed for Amharic language based onrnacoustic and linguistic features.rnThe classification performance is based on extracted features. We used a baseline set of 384rnacoustic features and for linguistic analysis techniques from text we used key word spotting,rnnegation handling and sentiment analysis with emotion generation rules. Combining thosernfeatures, we achieved an accuracy of 64..2% in identifying Happiness, Surprise, Anger, Sadness,rnFear, Disgust and Neutral emotions.